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State of the art and practice in AI in education
Open accessAbstract
Recent developments in Artificial Intelligence (AI) have generated great expectations for the future impact of AI in education and learning (AIED). Often these expectations have been based on misunderstanding current technical possibilities, lack of knowledge about state‐of‐the‐art AI in education, and exceedingly narrow views on the functions of education in society. In this article, we provide a review of existing AI systems in education and their pedagogic and educational assumptions. We develop a typology of AIED systems and describe different ways of using AI in education and learning, show how these are grounded in different interpretations of what AI and education is or could be, and discuss some potential roadblocks on the AIED highway.
Checked 2026-10-09 · 3 references not checked yet
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This review argues that AIED is not a single homogeneous field but a typology of student-, teacher-, and institution-focused applications, most of which remain grounded in narrow instructionist and mastery-learning assumptions. It concludes that robust evidence for AIED’s broad efficacy is elusive, while ethical, personalisation, techno-solutionist, colonial, and commercialisation roadblocks remain unresolved.
Key findings
- The paper develops a taxonomy of AIED into student-focused, teacher-focused, and institution-focused systems, marking applications as speculative, researched, or commercially available; examples include ITS, chatbots, automatic essay writing, plagiarism detection, classroom monitoring, admissions, and e-proctoring.
- Most existing AIED is student-focused and knowledge-based, especially ITS built on mastery learning and the “2-sigma effect”; however, evidence is contested: meta-reviews show modest learning gains, independent at-scale evaluations are rare, and the What Works Clearinghouse reported that Cognitive Tutor had mixed effects on algebra, no discernible effects on general mathematics, and potentially negative effects on geometry.
- The paper identifies major roadblocks: ethics and human rights, limited interpretations of personalisation, weak efficacy evidence, techno-solutionism, AIED colonialism, and commercialisation of education; it notes that most AIED research and development has occurred without serious engagement with ethical consequences.
- It distinguishes data-driven AI from knowledge-based AI, noting that knowledge-based AI still underpins most existing AIED systems, while data-driven AI has advanced through massive computation and data—e.g., GPT-3 has 175 billion adjustable parameters, was trained on about 400 billion words, and its training was estimated to generate 85,000 kg of CO₂ emissions.
Methods
- Design: conceptual review and taxonomy development, not an empirical study; the paper reviews existing AI systems in education and their pedagogic and educational assumptions.
- Data/sample: existing literature, examples of AIED systems, and policy/ethics sources; no primary sample or experimental dataset is reported.
- Analysis: narrative synthesis that classifies AIED applications into student-, teacher-, and institution-focused categories and discusses potential roadblocks.
Limitations
- The paper states that it is not possible to draw aggregate inferences or make grand claims about AIED’s efficacy because AIED applications vary widely; it also notes that its taxonomy categories can be argued over and that many applications remain speculative.
- It states that robust independent evidence remains elusive, most impact studies are conducted by developers, often with small numbers of learners, and most large independent studies are US-based, limiting transferability.
- Evident from the text: the review does not report a systematic search strategy, inclusion criteria, or formal quality appraisal, so its coverage and conclusions depend on the authors’ selected examples and synthesis.
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Claims the paper makes
61 Own interpretation · 56 Not checked yet · 13 No basis given
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In the paper
“Despite their potential for supporting student learning, and probably because of the difficulties of automatically providing accurate and helpful feedback, there remain few commercial examples of AFA.”
§ State of the art and practice in AI in education > 3.1.7 | Automatic formative assessment
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In the paper
“There are no known commercial ELEs, although a research example is FractionsLab (IOE, 2018).”
§ State of the art and practice in AI in education > 3.1.10 | Exploratory learning environments (ELE)
What the sources say
- Not checked yetcites IOE, 2018
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In fact, if for analysis we ignore the overlaps, there are few examples of genuinely teacher focused AIED.”
§ State of the art and practice in AI in education > 3.2 | Teacher focused AIED
plain- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Over the past decade, the situation has changed, and AIED is now also a focus of commercial interests.”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Instead, over the last three decades, a key conceptual starting point for student-focused AIED has been 'mastery learning', a pedagogic model advanced by Benjamin Bloom (Bloom, 1968;cf. Guskey, 2012).”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
What the sources say
- Not checked yetcites Bloom, 1968
- Not checked yetcites Guskey, 2012
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This history of AI is relevant to AIED as many of the most researched and visible student-focused AI systems are direct descendants of these ideas.”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
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In the paper
“Bloom later showed that individualised tutoring combined with mastery learning leads to two standard deviations higher learning gains than traditional whole-class teaching (Bloom, 1984).”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
What the sources say
- Not checked yetcites Bloom, 1984
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Classroom-level aggregation, therefore, potentially distorts existing meta-analyses.”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
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In the paper
“Meanwhile, at many universities, AI-assisted systems are also being used to monitor a student's movements through the campus (sometimes by means of a mobile phone app), what they download from the online learning management system, what they buy from the cafeterias, and much more besides (Moriarty-Mclaughlin, 2020).”
§ State of the art and practice in AI in education > 3.2.3 | Classroom monitoring
What the sources say
- Not checked yetcites Moriarty-Mclaughlin, 2020
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In other words, robust independent evidence (to the standard expected in, for example, medical research) remains elusive in AIED research.”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
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- Probably not a substantial claim. 68
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We have not been able to identify currently available commercial examples of DBTS.”
§ State of the art and practice in AI in education > 3.1.9 | Dialogue-based tutoring systems (DBTS)
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In the paper
“However, to date such a tool has received very little research effort.”
§ State of the art and practice in AI in education > 3.1.11 | AI-assisted lifelong learning assistants
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“There are few researched and commercially available examples of this.”
§ State of the art and practice in AI in education > 3.1.8 | Learning network orchestrators
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“In fact, some influential ITS, such as the ASSISTments system, started explicitly as a system for high-stakes test preparation (Heffernan & Heffernan, 2014); and many widely used Chinese AIED systems are also focused on teaching for the test (Knox, 2020).”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
What the sources say
- Not checked yetcites Heffernan & Heffernan, 2014
- Not checked yetcites Knox, 2020
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In much of the existing student-focused AIED research and development, the ultimate rationale of using AI has been that it can lead to learning gains in specific knowledge domains independently of human teachers.”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Many current commercial AI systems developed for education, so-called Intelligent Tutoring Systems (ITS), focus on providing automated, adaptive and individualised instruction, an approach that we explore in more detail below.”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
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In the paper
“Research and development in AI for education (AIED) has to a large extent been driven by computer scientists (Chen et al., 2020;Williamson & Eynon, 2020;Zawacki-Richter et al., 2019).”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
What the sources say
- Not checked yetcites Chen et al., 2020
- Not checked yetcites Williamson & Eynon, 2020
- Not checked yetcites Zawacki-Richter et al., 2019
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In the paper
“The technological origins of student focused AIED, however, can be traced to the work of Sidney Pressey, B. F. Skinner, Gordon Pask, and Jaime Carbonell (Holmes et al., 2019).”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
What the sources say
- Not checked yetcites Holmes et al., 2019
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In the paper
“Learning as a developmental and socio-cultural phenomenon, however, has only recently become a more common topic in AIED (e.g., Thomas & Porayska-Pomsta, 2022).”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
What the sources say
- Not checked yetcites Thomas & Porayska-Pomsta, 2022
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“These two latter functions have received comparatively little attention from AIED researchers.”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
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In the paper
“The close historical link between AI and cognitive science (Gardner, 1985) has meant that many influential AI systems in education have been built on cognitive architectures, themselves based on the idea that the human brain is an information processor.”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
What the sources say
- Not checked yetcites Gardner, 1985
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In the paper
“In more recent AIED research, the impact of human tutoring has been found to be below one standard deviation, which the research also suggests has already been achieved by some ITS (VanLehn, 2011).”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
What the sources say
- Not checked yetcites VanLehn, 2011
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In the paper
“Many higher education institutions, mainly in the US, use commercially available AI-assisted admissions software to support their admission processes-however, not without controversy (Pangburn, 2019).”
§ State of the art and practice in AI in education > 3.3.1 | Admissions
What the sources say
- Not checked yetcites Pangburn, 2019
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In the paper
“The human brain, on the other hand, operates with about 20 Watts of energy, which suggests that data-driven AI is based on different principles than human intelligence (Tuomi, 2020).”
§ State of the art and practice in AI in education > 2.1 | Data-driven AI
What the sources say
- Not checked yetcites Tuomi, 2020
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“In fact, e-Proctoring is probably one of the clearest examples of using AI to automate poor pedagogic practices, rather than using it to develop innovative approaches.”
§ State of the art and practice in AI in education > 3.3.2 | E-proctoring
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In the paper
“All state-of-the-art data-driven AI systems, therefore, work in the following way.First, the system is given some input data, and it is allowed to make a prediction.The error in prediction is used to calculate a 'loss' that the system tries to minimise during its training phase by adapting its parameter values.”
§ State of the art and practice in AI in education > 2.1 | Data-driven AI
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In the paper
“As noted in the introduction, the purported benefits of AI in education have received much visibility (e.g., OECD, 2020OECD, , 2021)).”
§ State of the art and practice in AI in education > 4 | ROADB LO CK S ON THE AI HI G HWAY
What the sources say
- Not checked yetcites OECD, 2020
- Not checked yetcites OECD, 2021
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, mastery learning requires individual-level differentiation or 'personalisation' of instruction, for which AIED systems have been proposed and designed as an answer.”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
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In the paper
“One classical definition specifies that AI is research that develops technologies that can do things that would require intelligence if done by humans (Minsky, 1969).”
§ State of the art and practice in AI in education > 2 | WHAT IS AI?
What the sources say
- Not checked yetcites Minsky, 1969
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In the paper
“It is, however, important to note that useful and usable definitions of AI depend on what they are used for.”
§ State of the art and practice in AI in education > 2 | WHAT IS AI?
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In the paper
“Nonetheless, the 2-sigma effect also provides a rarely contested implicit model of the function of education among AIED researchers, which ignores Biesta's socialisation and individuation functions.”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
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In the paper
“Energy consumption is now widely understood to be a major challenge for data-driven AI (Strubell et al., 2019).”
§ State of the art and practice in AI in education > 2.1 | Data-driven AI
What the sources say
- Not checked yetcites Strubell et al., 2019
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In the paper
“Data-driven AI has seen extraordinary advances during the last decade because of three key factors (Tuomi, 2018).”
§ State of the art and practice in AI in education > 2.1 | Data-driven AI
What the sources say
- Not checked yetcites Tuomi, 2018
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In the paper
“Such a vision of the AI-enabled future neatly summarises the beliefs about AIED held by many of its keenest advocates.”
§ State of the art and practice in AI in education > 4 | ROADB LO CK S ON THE AI HI G HWAY
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In the paper
“Ambiguous results, methodological and practical challenges, lack of independence and scale, and more fundamental questions about the aims of education have left ample space to contest any generic claims about the benefits of student-focused AIED.”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Data-driven AI has produced impressive results in the last decade in computer vision, natural language processing, robotics, and in many other areas widely reported in the media.”
§ State of the art and practice in AI in education > 2.1 | Data-driven AI
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In the paper
“In fact, as most current AI systems essentially are behaviouristic stimulus-response systems, it has also been suggested that AI should more appropriately denote Artificial Instincts (Tuomi, 2018).”
§ State of the art and practice in AI in education > 2 | WHAT IS AI?
What the sources say
- Not checked yetcites Tuomi, 2018
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In the paper
“AI is applied in education (AIED) in multiple ways.”
§ State of the art and practice in AI in education > 3 | A TA XONOMY OF AIED
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In the paper
“There are many studies that have shown learning gains when ITS have been used in the classroom (cf.du Boulay, 2019), but the results are not unambiguous.”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
What the sources say
- Not checked yetcites du Boulay, 2019
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The market is now dominated by Turnitin (Turnitin, 2022), with its various tools such as iThenticate and Ouriginal, but also detection software such as Plagiarism Checker X (Plagiarism Checker X, 2022) and the more student oriented Grammarly's plagiarism checker (Grammarly, 2022b) are extensively used.”
§ State of the art and practice in AI in education > 3.2.1 | Plagiarism detection
What the sources say
- Not checked yetcites Turnitin, 2022
- Not checked yetcites Plagiarism Checker X, 2022
- Not checked yetcites Grammarly, 2022b
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“More importantly, it is not entirely clear what such 'working' would comprise.”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“AI refers to machine-based systems that can, given a set of human-defined objectives, make predictions, recommendations, or decisions that influence real or virtual environments.AI systems interact with us and act on our environment, either directly or indirectly.Often, they appear to operate autonomously, and can adapt their behaviour by learning about the context.(UNICEF, 2021, p. 16)”
§ State of the art and practice in AI in education > 2 | WHAT IS AI?
What the sources say
- Not checked yetcites UNICEF, 2021
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Nonetheless, what is particularly interesting is that the learner is in charge, deciding what they want to learn, while the AI (unlike with an ITS) plays a supporting role.”
§ State of the art and practice in AI in education > 3.1.8 | Learning network orchestrators
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Only rare examples exist of independent evaluations at scale (e.g., Roschelle et al., 2016), while meta-level reviews (e.g., Kulik & Fletcher, 2016;Ma et al., 2014) show modest learning gain impacts across many relatively small settings.”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
What the sources say
- Not checked yetcites Roschelle et al., 2016
- Not checked yetcites Kulik & Fletcher, 2016
- Not checked yetcites Ma et al., 2014
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, AI systems always make their recommendations, predictions and decisions based on objectives that are specified by the system designer at the time the system is developed.”
§ State of the art and practice in AI in education > 2 | WHAT IS AI?
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Accordingly, it is not possible either to draw aggregate inferences or, for example, make grand claims about AIED's efficacy or otherwise.”
§ State of the art and practice in AI in education > 3 | A TA XONOMY OF AIED
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
None of the sources cited for this claim have been checked yet.
In the paper
“However, in so doing, they effectively take over teaching tasks, potentially reducing teachers to a functional role (Guilherme, 2019;Selwyn, 2019).”
§ State of the art and practice in AI in education > 3.2.5 | AI teaching and assessment assistant
What the sources say
- Not checked yetcites Guilherme, 2019
- Not checked yetcites Selwyn, 2019
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This model underpins most ITS, as well as the notion that AI can 'personalise' learning.”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Training OpenAI's GPT-3 has been estimated to have required as much energy as driving a car to the moon and back, thus generating an equivalent of 85,000 kg of CO 2 emissions (Quach, 2020).”
§ State of the art and practice in AI in education > 2.1 | Data-driven AI
What the sources say
- Not checked yetcites Quach, 2020
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Historically, the emergence of new key technologies have led to exaggerated and over-optimistic expectations, subsequent economic crashes, and the gradual articulation of technological possibilities over relatively long periods of time (Nemorin et al., 2022;Perez, 2002).”
§ State of the art and practice in AI in education > 1 | INTRODUC TI ON 1.| A brief history of the future of AI in education
What the sources say
- Not checked yetcites Nemorin et al., 2022
- Not checked yetcites Perez, 2002
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Inevitably, as this system involves human tutors, it is relatively expensive to scale.”
§ State of the art and practice in AI in education > 3.1.8 | Learning network orchestrators
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Data-driven AI might have great potential in education, depending on the intentions of the system, but knowledge-based AI still underpins most existing AIED systems.”
§ State of the art and practice in AI in education > 2 | WHAT IS AI?
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“In fact, as discussed in the introduction, the development of technologies to personalise learning to the strengths and weaknesses of individual students began almost 100 years ago, with the so-called teaching machines devised by Sidney Pressey and B. F. Skinner (Watters, 2021).”
§ State of the art and practice in AI in education > 4.2 | Personalisation
What the sources say
- Not checked yetcites Watters, 2021
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“AIED colonialism might involve the adoption of AIED tools created in one context in other places, leading to market and economic gains for the Global North corporations, with the extraction of local data and capital out of the host country (Nemorin et al., 2022).”
§ State of the art and practice in AI in education > 4.5 | AIED colonialism
What the sources say
- Not checked yetcites Nemorin et al., 2022
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Early on in the COVID-19 pandemic, much education moved online, as did many examinations-which led to many exam-monitoring, or e-Proctoring, companies seeing their businesses grow massively (Nigam et al., 2021).”
§ State of the art and practice in AI in education > 3.3.2 | E-proctoring
What the sources say
- Not checked yetcites Nigam et al., 2021
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“More subtly, it might simply involve the language in which most classroom AIED tends to be trained-mainly American English (Cotterell et al., 2020).”
§ State of the art and practice in AI in education > 4.5 | AIED colonialism
What the sources say
- Not checked yetcites Cotterell et al., 2020
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, while the immediate cohort in such a context might benefit from being given access to an AIED tool, there are many challenges.”
§ State of the art and practice in AI in education > 4.4 | Techno-solutionism
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“It has long been thought, ever since Socrates argued that writing led to forgetfulness (Plato, 257 C.E.), that technology has a significant impact on human development and cognition.”
§ State of the art and practice in AI in education > 4.3 | Efficacy and impact
What the sources say
- Not checked yetcites Plato, 257 C.E.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“As the many articles published in the International Journal of Artificial Intelligence in Education show, academic researchers have conducted many studies into the efficacy of various AIED systems.”
§ State of the art and practice in AI in education > 4.3 | Efficacy and impact
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For various reasons, these machines failed to be widely accepted, and the personalised learning agenda more or less disappeared.”
§ State of the art and practice in AI in education > 4.2 | Personalisation
What the sources say
- Not checked yetcites Watters, 2021
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“For example, while one study has suggested that greater use of Global Positioning System (GPS) navigation devices and applications leads to a decline in spatial memory (Dahmani & Bohbot, 2020), another has shown that the use of smartphone alerts frees cognitive resources for other tasks (Fröscher et al., 2022).”
§ State of the art and practice in AI in education > 4.3 | Efficacy and impact
What the sources say
- Not checked yetcites Dahmani & Bohbot, 2020
hedgeddirect- Asserted: fails — The authors may not put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The authors may not put it forward as true. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While ITS might be useful when the student is without a teacher or their peers, perhaps when completing homework, it is not clear why some schools use them in classrooms, which are by definition social spaces.”
§ State of the art and practice in AI in education > 4.2 | Personalisation
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The personalisation of learning pathways offered by much current AIED, however, is a very limited interpretation of personalisation.”
§ State of the art and practice in AI in education > 4.2 | Personalisation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“Despite the meaning of personalised learning remaining unclear (Holmes, Anastopoulou, et al., 2018), it increasingly informs the mainstream education narrative (e.g., European Parliament, 2021; UNICEF, 2022).”
§ State of the art and practice in AI in education > 4.2 | Personalisation
What the sources say
- Not checked yetcites Holmes, Anastopoulou, et al., 2018
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AIED corporations are increasingly selling their AIED tools globally, creating what has been called an AIED colonialism: Global North companies exporting their AIED tools into contexts in the Global South, creating asymmetries in power across and between nations.”
§ State of the art and practice in AI in education > 4.5 | AIED colonialism
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“This is at an early stage of development, nonetheless there is a growing body of research into how AI might assist classroom orchestration (Song, 2021).”
§ State of the art and practice in AI in education > 3.2.6 | Classroom orchestration
What the sources say
- Not checked yetcites Song, 2021
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These gains and extractions may begin with individual schools embedding AIED tools into teachers' everyday practices before expanding to draw in entire state education systems in which single products are adopted across all schools.”
§ State of the art and practice in AI in education > 4.5 | AIED colonialism
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“The overall conclusion of these metareviews and analyses is that AIED systems perform better than […] human teachers working in large classes.They perform slightly worse than one-on-one human tutors […].”
§ State of the art and practice in AI in education > 4.3 | Efficacy and impact
What the sources say
- Not checked yetcites du Boulay, 2016
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“Autograders have been used for the assessment of written tasks (e.g., the US SATs) (Ramesh & Sanampudi, 2021), and in computer science and mathematics courses.”
§ State of the art and practice in AI in education > 3.2.4 | Automatic summative assessment
What the sources say
- Not checked yetcites Ramesh & Sanampudi, 2021
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Most of these large independent studies have been conducted in the US, limiting their transferability to other countries.”
§ State of the art and practice in AI in education > 4.3 | Efficacy and impact
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The vast majority of impact studies have been conducted by the developers of the particular technology being studied (increasingly from commercial organisations), and most often with relatively small numbers of learners.”
§ State of the art and practice in AI in education > 4.3 | Efficacy and impact
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“This is exacerbated by the fact that the overwhelming balance of AIED research is also carried out in the Global North, and rarely addresses cultural diversity or local policies and practices in any meaningful way (Blanchard, 2015).”
§ State of the art and practice in AI in education > 4.5 | AIED colonialism
What the sources say
- Not checked yetcites Blanchard, 2015
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It re-emerged decades later, mainly from Silicon Valley, partly because the internet made mass customisation possible across a broad range of industries.”
§ State of the art and practice in AI in education > 4.2 | Personalisation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“However, these tools are hugely controversial (Kelley, 2021).”
§ State of the art and practice in AI in education > 3.3.2 | E-proctoring
What the sources say
- Not checked yetcites Kelley, 2021
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“One important potential impact of AIED is on human cognition and brain development.”
§ State of the art and practice in AI in education > 4.3 | Efficacy and impact
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Third, education is also about collaboration and the other social interaction aspects of teaching and learning, which are often ignored by current ITS and most other AIED.”
§ State of the art and practice in AI in education > 4.2 | Personalisation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“For example, while one study has suggested that greater use of Global Positioning System (GPS) navigation devices and applications leads to a decline in spatial memory (Dahmani & Bohbot, 2020), another has shown that the use of smartphone alerts frees cognitive resources for other tasks (Fröscher et al., 2022).”
§ State of the art and practice in AI in education > 4.3 | Efficacy and impact
What the sources say
- Not checked yetcites Fröscher et al., 2022
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Second, the socalled individual pathways provided by almost all AIED systems are mostly based on the averages of prior learners.”
§ State of the art and practice in AI in education > 4.2 | Personalisation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Inevitably, however, this impact is likely to be complex.”
§ State of the art and practice in AI in education > 4.3 | Efficacy and impact
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Such tools have a clear administrative function and share much with business-orientated Artificial Intelligence.”
§ State of the art and practice in AI in education > 3.3 | Institution focused AIED
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“In recent years, for AI in general there has been a growing focus on ethics, resulting in more than 80 sets of ethical AI principles (Ayling & Chapman, 2021;Jobin et al., 2019;Tsamados et al., 2022).”
§ State of the art and practice in AI in education > 4.1 | Ethics
What the sources say
- Not checked yetcites Ayling & Chapman, 2021
- Not checked yetcites Jobin et al., 2019
- Not checked yetcites Tsamados et al., 2022
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although the general ethical concerns related to AI are now widely debated, education has important social roles and aims at human development, which makes the related ethical challenges particularly difficult both conceptually and in practice.”
§ State of the art and practice in AI in education > 4.1 | Ethics
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: Education has important social roles and aims at human development.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Most importantly, AIED in such contexts might address the apparent symptoms of the problem (for example, learners not receiving a quality education), but not necessarily the underlying and long-term socio-political causes (the lack of experienced and qualified teachers).”
§ State of the art and practice in AI in education > 4.4 | Techno-solutionism
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Many rural areas do not yet have the necessary infrastructure (electricity and access to the Internet).
- Rests on an assumption: Rarely are there the skilled support staff needed for the deployment, management and support of the required hardware and software in rural areas.
None of the sources cited for this claim have been checked yet.
In the paper
“Nonetheless, AI is increasingly being used to support admissions (Marcinkowski et al., 2020), often using tools provided by commercial companies such as Salesforce (Salesforce, 2022).”
§ State of the art and practice in AI in education > 3.3.1 | Admissions
What the sources say
- Not checked yetcites Marcinkowski et al., 2020
- Not checked yetcites Salesforce, 2022
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, most teachers personalise their teaching moment by moment in response to each individual student, helping the student (if the particular education system permits) to self-actualise, to become the best that they can be.”
§ State of the art and practice in AI in education > 4.2 | Personalisation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“For example, a study on the complex ways in which Google binds AIED users and developers into its infrastructure has shown that the Google Classroom has become both a global collector of student data and also a critical control point where the rules of participation are defined (Perrotta et al., 2021;Williamson, 2021).”
§ State of the art and practice in AI in education > 4.6 | Commercialisation of education
What the sources say
- Not checked yetcites Perrotta et al., 2021
- Not checked yetcites Williamson, 2021
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“All our images of the future are based on our understanding and construction of history.”
§ State of the art and practice in AI in education > 5 | CON CLUDING VIS ION
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As the progress in data-driven AI has led to exponentially growing computational requirements, it is now increasingly understood that the future of AI cannot necessarily be predicted by extrapolating the developments of the last decade.”
§ State of the art and practice in AI in education > 5 | CON CLUDING VIS ION
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, while original research is undertaken in academia with the explicit aim of enhancing teaching and learning, today's commercial organisations by definition focus on generating profits.”
§ State of the art and practice in AI in education > 4.6 | Commercialisation of education
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AIED colonialism might also involve the imposition of particular approaches to pedagogy-at present often instructionist and behaviourist-as embedded in most current commercial AIED tutoring systems.”
§ State of the art and practice in AI in education > 4.5 | AIED colonialism
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Many recent breakthroughs in datadriven AI, such as the capability to locate a cat in a picture or distinguish words in a spoken sentence, are simple tasks for an infant already years before the child enters school.”
§ State of the art and practice in AI in education > 5 | CON CLUDING VIS ION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“This suggests that the future of AIED should be understood from the point of view of AI-supported augmentation of human cognition and learning, an approach that has been an important line of thinking in AI throughout its history (Bush, 1945;Engelbart, 1963;Winograd & Flores, 1986).”
§ State of the art and practice in AI in education > 5 | CON CLUDING VIS ION
What the sources say
- Not checked yetcites Bush, 1945
- Not checked yetcites Engelbart, 1963
- Not checked yetcites Winograd & Flores, 1986
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Data-driven AI provides important basic information processing functionality, such as pattern recognition.”
§ State of the art and practice in AI in education > 5 | CON CLUDING VIS ION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Commercial AIED organisations are not only shaping individual learners but are also beginning to influence governance and national policies.”
§ State of the art and practice in AI in education > 4.6 | Commercialisation of education
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In educational contexts, the development of AI, therefore, may more constructively be seen as a joint development of human and artificial cognition.”
§ State of the art and practice in AI in education > 5 | CON CLUDING VIS ION
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Beyond limiting interoperability, this has potentially important consequences for the social control of education and educational innovation.”
§ State of the art and practice in AI in education > 4.6 | Commercialisation of education
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: Commercial organisations rarely share information about their proprietary systems and their effectiveness.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While learnerfocused AI has been the subject of research for around 40 years, almost a decade ago it shifted from having been a focus of research labs to be developed into commercial products by a growing number of multi-million-dollarfunded AIED companies.”
§ State of the art and practice in AI in education > 4.6 | Commercialisation of education
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In short, the commercialisation of education through an AIED back door is fraught with both practical and ideological issues.”
§ State of the art and practice in AI in education > 4.6 | Commercialisation of education
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is mostly these products that are being implemented in schools around the world, frequently by government (local and national) agencies.”
§ State of the art and practice in AI in education > 4.6 | Commercialisation of education
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The societal and cultural impact may also exceed the more surface level economic ones.”
§ State of the art and practice in AI in education > 4.6 | Commercialisation of education
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A final potential roadblock noted in this article is the commercialisation of education by stealth.”
§ State of the art and practice in AI in education > 4.6 | Commercialisation of education
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The future of AIED, therefore, is also an economic and even a geopolitical issue.”
§ State of the art and practice in AI in education > 4.6 | Commercialisation of education
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Finally, colonialism might also fall along a spectrum.”
§ State of the art and practice in AI in education > 4.5 | AIED colonialism
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The authors state this as an assumption.
AI is a specific field of inquiry and development, not simply a type of intelligence that is artificial.
“a name that we capitalise to highlight that it is a specific field of inquiry and development, and not simply a type of intelligence that is artificial.”
The paper relies on this without support.
In education, knowledge-based AI continues to play a central role.
“In education, however, knowledge-based AI continues to play a central role.”
The paper relies on this without support.
Commercially available intelligent tutoring systems (ITS) are the most common applications of AI in education, and probably the most well-funded.
“The commercially available so-called intelligent tutoring systems (ITS) are the most common applications of AI in education, and probably the most well-funded.”
The paper relies on this without support.
Automatic summative assessment is a well-funded area of research, second only to ITS, and extensively commercialised.
“For this reason, automatic summative assessment (sometimes also known as 'autograders') is a well-funded area of research, second only to ITS, and extensively commercialised.”
The paper relies on this without support.
The use of automatic scoring, especially when the assessment is high-stakes, remains controversial.
“Nonetheless, the use of automatic scoring, especially when the assessment is high-stakes, remains controversial.”
The paper relies on this without support.
Currently no AI system is capable of the depth of interpretation or accuracy of analysis that a teacher can give.
“A key problem is that currently no AI system is capable of the depth of interpretation or accuracy of analysis that a teacher can give, instead typically relying on surface features of the writing or other output.”
The paper relies on this without support.
ITS and other dashboard-enabled AIED are mostly student-focused.
“In any case, ITS and other dashboard-enabled AIED are all mostly student-focused.”
The paper relies on this without support.
Commercially available plagiarism detection services are widely used by educators, and machine learning methods have been increasingly adapted for these systems during the last decade.
“Commercially available plagiarism detection services are widely used by educators, and machine learning methods have been increasingly adapted for these systems during the last decade.”
The paper relies on this without support.
In a few contexts, researched and commercially available AI-assisted systems are increasingly being used for classroom-based student monitoring.
“In a few contexts, researched and commercially available AI-assisted systems are increasingly being used for classroom-based student monitoring.”
The paper relies on this without support.
Similar EEG headsets are widely used in Chinese schools, where both teachers and parents can review student brain activity over the internet.
“Similar headsets are widely used also in Chinese schools, where both teachers and parents can review student brain activity over the internet.”
The paper relies on this without support.
High-stakes testing is one of the two high-risk use cases in the proposed EU AI Act.
“In fact, high-stakes testing is one of the two high-risk use cases in the proposed EU AI Act, and so would be regulated by its provisions.”
The paper relies on this without support.
By 2020, the GRADE admissions system had been dropped because it was quietly replicating the very problems that it aimed to address.
“However, by 2020, GRADE had been dropped because it was quietly replicating the very problems that it aimed to address.”
The paper relies on this without support.
There are already several commercial organisations that offer automatic essay writing (AEW) tools for students.
“There are already several commercial organisations that offer automatic essay writing (AEW) tools for students that, in response to a prompt such as an essay question, can automatically generate individual paragraphs or entire essays.”
The paper relies on this without support.
Whether AEW tools support or undermine student learning is unclear.
“However, whether AEW tools support or undermine student learning is unclear.”
The paper relies on this without support.
VR and AR simulations and digital games-based learning are frequently combined with AI and increasingly used in educational settings.
“Although perhaps not traditionally thought of as AI technologies, commercially available Virtual Reality (VR) and Augmented Reality (AR) simulations and digital games-based learning are frequently combined with AI machine learning, image recognition and natural language processing, and are increasingly being used in educational settings.”
The paper relies on this without support.
Google has developed more than a thousand VR and AR Expeditions suitable for educational contexts.
“Google has developed more than a thousand VR and AR Expeditions suitable for educational contexts.”
The paper relies on this without support.
Written essays remain an important component of educational assessment around the world, yet passing off someone else's writing as your own has long been common.
“Written essays remain an important component of educational assessment around the world, yet passing off someone else's writing as your own has long been common.”
The paper relies on this without support.
The Internet has made passing off someone else's writing as your own increasingly easy, with online commercial essay mills offering bespoke essays on any topic.
“The Internet has made this increasingly easy, with online commercial essay mills offering bespoke essays on any topic.”
The paper relies on this without support.
Exploratory learning environments provide an alternative to the step-by-step approach adopted by ITS and DBTS.
“Exploratory learning environments (ELEs) provide an alternative to the step-by-step approach adopted by ITS and DBTS.”
The paper relies on this without support.
Many recent AI-driven ELEs provide automatic feedback, addressing misconceptions and proposing alternative approaches during exploration.
“However, this is where AI comes in, with many recent AI-driven ELEs providing automatic feedback, addressing misconceptions and proposing alternative approaches during exploration.”
The paper relies on this without support.
The idea of AI-assisted admissions is to reduce costs while making the admissions system more equitable, by helping to remove unseen human biases.
“The idea is to reduce costs while making the admissions system more equitable, by helping to remove unseen human biases (such as groupthink and racial and gender biases) that can impact decisions.”
The paper relies on this without support.
E-proctoring aims to assure academic integrity by using AI-assisted cameras and microphones to automatically monitor students while they complete an online examination.
“E-proctoring aims to assure academic integrity by using AI-assisted cameras and microphones to automatically monitor students-scanning their faces and tracking keystrokes and mouse movements-while they complete an online examination.”
The paper relies on this without support.
Education has important social roles and aims at human development.
“Although the general ethical concerns related to AI are now widely debated, education has important social roles and aims at human development, which makes the related ethical challenges particularly difficult both conceptually and in practice.”
The paper relies on this without support.
Many rural areas do not yet have the necessary infrastructure (electricity and access to the Internet).
“To begin with, many rural areas do not yet have the necessary infrastructure (electricity and access to the Internet).”
The paper relies on this without support.
Rarely are there the skilled support staff needed for the deployment, management and support of the required hardware and software in rural areas.
“Even when this is available, rarely are there the skilled support staff needed for the deployment, management and support of the required hardware and software.”
The paper relies on this without support.
The way in which problems are articulated often depends on the interests of technology providers.
“In practice, the way in which problems are articulated often depends on the interests of technology providers.”
The paper relies on this without support.
The deeper social and cultural factors are rarely addressed because they are difficult to change without broad stakeholder participation and policy change.
“The deeper social and cultural factors are rarely addressed as they are difficult to change without a broad participation of the stakeholders and policy change.”
The paper relies on this without support.
Commercial organisations rarely share information about their proprietary systems and their effectiveness.
“Commercial organisations rarely share information about their proprietary systems and their effectiveness.”
The paper relies on this without support.
Data-driven AI has important scale benefits, and in the current networked ecosystems this potentially leads to natural monopolies.
“Data-driven AI, in particular, has important scale benefits, and in the current networked ecosystems this potentially leads to natural monopolies.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“From the standpoint of sustainable higher education, AI literacy has been increasingly framed as a precondition for lifelong learning and adaptive citizenship in a rapidly evolving technological environment [6,7,26].”
From Toward Sustainable AI Literacy in Higher Education: A Delphi–AHP Approach to Identifying Key Components of AI/Data Extracurricular Programs (2026) · cites this paper as [26]
Experimental — accuracy not yet measuredIn the citing paper
“These results align with studies that demonstrate how AI can streamline administrative duties, generate personalized learning analytics, and support instructional planning [15,17].”
From Systematic review of artificial intelligence in education: trends, benefits, and challenges (2025) · cites this paper as [15]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ State of the art and practice in AI in education > 3 | A TA XONOMY OF AIED
“Dialogue-based Tutoring Systems (DBTS) * * *”
§ State of the art and practice in AI in education > 3.1.9 | Dialogue-based tutoring systems (DBTS)
“We have not been able to identify currently available commercial examples of DBTS.”
Citations
Cites
Language Models are Few-Shot Learners
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The perceptron: A probabilistic model for information storage and organization in the brain.
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Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242
The global landscape of AI ethics guidelines
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On understanding computers and cognition: A new foundation for design
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Energy and Policy Considerations for Deep Learning in NLP
Strubell, E., Ganesh, A., & McCallum, A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1355 · Cited by 3,684
The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring
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AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications
Rashid, A. B., & Kausik, M. A. K. (2024) · Hybrid Advances · doi:10.1016/j.hybadv.2024.100277 · Cited by 764
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“While AI automates tasks and provides data-driven insights, it lacks the empathy, creativity, and nuanced understanding of human educators (Holmes & Tuomi, 2022).”
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“Higher education institutions must actively define the usage and scope of AI in education for international students [1].”
BackgroundMohamed, A. M. (2024) · Education and Information Technologies · doi:10.1007/s10639-023-11917-z · Cited by 387
“According to a study by Holmes and Tuomi ( 2022 ), AI-powered language learn - ing tools, such as chatbots, can greatly enhance the language learning process by providing immediate feedback and personalized learning experiences.”
Hadi Mogavi, R., Deng, C., Juho Kim, J., Zhou, P., D. Kwon, Y., Hosny Saleh Metwally, A., Tlili, A., Bassanelli, S., Bucchiarone, A., Gujar, S., et al. (2024) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2023.100027 · Cited by 369
Towards hybrid human‐AI learning technologies
Molenaar, I. (2022) · European Journal of Education · doi:10.1111/ejed.12527 · Cited by 348
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“…platforms that provide personalized learning experiences to students, automated assessment, or AI-powered writing tools that enhance students ’ writing quality by providing real-time feedback on issues with grammar, punctuation, and style (Holmes & Tuomi, 2022; Zawacki-Richter et al., 2019).”
BackgroundGkintoni, E., Antonopoulou, H., Sortwell, A., & Halkiopoulos, C. (2025) · Brain Sciences · doi:10.3390/brainsci15020203 · Cited by 309
Kohnke, L., Moorhouse, B. L., & Zou, D. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100156 · Cited by 309
Hornberger, M., Bewersdorff, A., & Nerdel, C. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100165 · Cited by 276
Analyzing the students' views, concerns, and perceived ethics about chat GPT usage
Farhi, F., Jeljeli, R., Aburezeq, I., Dweikat, F. F., Al-shami, S. A., & Slamene, R. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100180 · Cited by 252
Systematic review of artificial intelligence in education: trends, benefits, and challenges
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“These results align with studies that demonstrate how AI can streamline administrative duties, generate personalized learning analytics, and support instructional planning [15,17].”
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Artificial Intelligence as a Disruptive Technology—A Systematic Literature Review
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McGrath, C., Cerratto Pargman, T., Juth, N., & Palmgren, P. J. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100139 · Cited by 195
A systematic review of AI-driven intelligent tutoring systems (ITS) in K-12 education
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“Despite the growing use of ITSs and the potential they offer to the education industry, stakeholders still struggle to navigate between the potential to improve learning outcomes and reduce inequities in education and the actual educational value, which remains uncertain 16 .”
Fine-tuning ChatGPT for automatic scoring
Latif, E., & Zhaı, X. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100210 · Cited by 244
Investigating the Use of an Artificial Intelligence Chatbot with General Chemistry Exam Questions
Clark, T. M. (2023) · Journal of Chemical Education · doi:10.1021/acs.jchemed.3c00027 · Cited by 186
Alam, A. (2023) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-99-1767-9_42 · Cited by 160
Sallam, M., Salim, N. A., Al-Tammemi, A. B., Barakat, M., Fayyad, D., Hallit, S., Harapan, H., Hallit, R., & Mahafzah, A. (2023) · Cureus · doi:10.7759/cureus.35029 · Cited by 130
Proactive and reactive engagement of artificial intelligence methods for education: a review
Mallik, S., & Gangopadhyay, A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1151391 · Cited by 139
“categorization: review articles that group research contributions based on some distinguishing factors, such as problem statement and solution methodology (Chassignol et al., 2018; ZawackiRichter et al., 2019; Ahmad et al., 2020, 2022; Chen L. et al., 2020; Yufeia et al., 2020; Huang J. et al., 2021; Lameras and Arnab…”
BackgroundDo we still need teachers? Navigating the paradigm shift of the teacher's role in the AI era
Gentile, M., Città, G., Perna, S., & Allegra, M. (2023) · Frontiers in Education · doi:10.3389/feduc.2023.1161777 · Cited by 189
“Of course, these issues are still being also debated in the scientific arena, where a steady increase in studies linking AI and education is evident (Floridi et al., 2018; Holmes and Tuomi, 2022).”
BackgroundHazzan-Bishara, A., Kol, O., & Levy, S. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13393-z · Cited by 225
“Artificial Intelligence (AI) refers to the simulation of human intelligence by machines, enabling interaction with humans to influence behavior and assist in accomplishing tasks beyond individual capabilities (Chintalapati & Pandey, 2022; Holmes & Tuomi, 2022; Uzir et al., 2023).”
Ou, A. W., Stöhr, C., & Malmström, H. (2024) · System · doi:10.1016/j.system.2024.103225 · Cited by 224
“Despite the widespread attention given to AI, precise definitions of it, not least in applied linguistics, are lacking (Bearman et al., 2022; Holmes & Tuomi, 2022).”
BackgroundHolmes, W., Stracke, C. M., Chounta, I., Allen, D., Baten, D., Dimitrova, V. G., Havinga, B., Norrmen-Smith, J., & Wasson, B. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-36336-8_12 · Cited by 119
Self-citationGenerative AI tools as educators’ assistants: Designing and implementing inquiry-based lesson plans
Moundridou, M., Matzakos, N., & Doukakis, S. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100277 · Cited by 176
Onciul, R., Tataru, C.-I., Dumitru, A. V., Crivoi, C., Serban, M., Covache-Busuioc, R.-A., Radoi, M. P., & Toader, C. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14020550 · Cited by 113
“These systems will be particularly impactful for individuals with learning disabilities, offering tailored support that maximizes their potential [297].”
Pursnani, V., Sermet, Y., Kurt, M., & Demir, I. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100183 · Cited by 106
Creative partnerships with generative AI. Possibilities for education and beyond
Creely, E., & Blannin, J. (2024) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2024.101727 · Cited by 141
Risks of AI-Assisted Learning on Student Critical Thinking
Çela, E., Fonkam, M. M., & Potluri, R. M. (2024) · International Journal of Risk and Contingency Management · doi:10.4018/ijrcm.350185 · Cited by 90
“Holmes and Tuomi (2022) believe that AI tools, combined with other technologies, can help create adaptive learning experiences tailored to individual student needs.”
Mouta, A., Llorente, A. M. P., & Sánchez, E. M. T. (2023) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00384-9 · Cited by 175
“Finally, most studies assessing the effects of technology are typically carried out by the creators of that technology, often affiliated with commercial entities and involving a limited number of participants (Holmes & Tuomi, 2022).”
Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Almuqren, L., Almgren, A. S., Alshimai, A., & Al‐Adwan, A. S. (2025) · IEEE Access · doi:10.1109/access.2025.3560567 · Cited by 111
Preparing future educators for AI-enhanced classrooms: Insights into AI literacy and integration
Kohnke, L., Zou, D., Ou, A. W., & Gu, M. M. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100398 · Cited by 86
AI in the classroom: Exploring students’ interaction with ChatGPT in programming learning
Güner, H., & Er, E. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13337-7 · Cited by 134
“…educational technologies can enhance student engagement by offering interactive and adaptive learning experiences, delivering instant and detailed feedback, and customizing instructional content to align with individual student needs and learning paces (Dai & Liu, 2024; Holmes & Tuomi, 2022).”
Flodén, J. (2024) · British Educational Research Journal · doi:10.1002/berj.4069 · Cited by 124
Artificial Intelligence Integration: Pedagogical Strategies and Policies at Leading Universities
Alqahtani, N., & Wafula, Z. (2025) · Innovative Higher Education · doi:10.1007/s10755-024-09749-x · Cited by 84
“For example, some teachers and students perceive it as a distinctive tool enhancing the educational paradigm (Holmes & Tuomi, 2022 ); others see it as a multifaced and complex tool.”
Inclusive education with AI: supporting special needs and tackling language barriers
Fitas, R. (2025) · AI and Ethics · doi:10.1007/s43681-025-00824-3 · Cited by 198
“Traditionally, schools have addressed this through bilingual aides, translated materi - als, or peer support, but such resources are often limited [ 29, 30 ].”
Chatbots and AI in Education (AIEd) tools: The good, the bad, and the ugly
Ifelebuegu, A. O., Kulume, P., & Cherukut, P. (2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.2.29 · Cited by 109
Wiboolyasarin, W., Wiboolyasarin, K., Tiranant, P., Jinowat, N., & Boonyakitanont, P. (2025) · Ampersand · doi:10.1016/j.amper.2025.100224 · Cited by 169
Kohnke, L., & Moorhouse, B. L. (2025) · Computers in Human Behavior · doi:10.1016/j.chb.2025.108600 · Cited by 75
Information and Media Literacy in the Age of AI: Options for the Future
Tiernan, P., Costello, E., Donlon, E., Parysz, M., & Scriney, M. (2023) · Education Sciences · doi:10.3390/educsci13090906 · Cited by 76
“Learner and Teacher Sourced Approach to Digital Competencies: Much of the research on AI in education has been dominated by computer scientists [49,50].”
BackgroundArtificial Intelligence and K-12 Education: Possibilities, Pedagogies and Risks
Mintz, J., Holmes, W., Liu, L., & Perez-Ortiz, M. (2023) · Computers in the Schools · doi:10.1080/07380569.2023.2279870 · Cited by 74
“(Holmes & Tuomi, 2022, p. 16) Lastly, institution-focused AI encompasses AI-enabled tools designed to assist educational institutions with tasks such as student recruitment, security, financial management, and other essential administrative functions.”
BackgroundStakeholder Perspectives on the Ethics of AI in Distance-Based Higher Education
Holmes, W., Iniesto, F., Anastopoulou, S., & Boticario, J. G. (2023) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v24i2.6089 · Cited by 67
“However, there remains little evidence at scale for the efficacy or impact of these applications (Holmes & Tuomi, 2022), and already multiple issues are beginning to reveal themselves.”
InfluentialSelf-citationBeyond Traditional Assessment: Exploring the Impact of Large Language Models on Grading Practices
Fagbohun, O., Iduwe, N. P., Abdullahi, M., Ifaturoti, A., & Nwanna, M. (2024) · Journal of Artificial Intelligence Machine Learning and Data Science · doi:10.51219/jaimld/oluwole-fagbohun/19 · Cited by 80
“Finite knowledge of LLMs and the need for regular updates to maintain relevance in a constantly evolving educational landscape present additional challenges 75-78 .”
Shamsuddinova, S., Heryani, P., & Naval, M. A. (2024) · International Journal of Educational Research · doi:10.1016/j.ijer.2024.102326 · Cited by 80
Gupta, P., Mahajan, R., Badhera, U., & Kushwaha, P. S. (2024) · The International Journal of Management Education · doi:10.1016/j.ijme.2024.101017 · Cited by 80
Almuhanna, M. A. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-13257-y · Cited by 101
“Holmes and Tuomi (2022) emphasize the importance of balanced adoption of AI by providing recommendations to enhance motivation and ethical responsibility.”
Zou, D., Xie, H., & Kohnke, L. (2025) · European Journal of Education · doi:10.1111/ejed.70117 · Cited by 60
Artificial Intelligence Across Industries
Yadav, U., & Shrawankar, U. N. (2024) · Advances in educational marketing, administration, and leadership book series · doi:10.4018/979-8-3693-5443-8.ch010 · Cited by 61
Opportunities and challenges of using generative AI to personalize educational assessment
Arslan, B., Lehman, B., Tenison, C., Sparks, J. R., López, A. A., Gu, L., & Zapata-Rivera, D. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1460651 · Cited by 52
Artificial Intelligence‐Supported Student Engagement Research: Text Mining and Systematic Analysis
Chen, X., Xie, H., Qin, S. J., Wang, F. L., & Hou, Y. (2025) · European Journal of Education · doi:10.1111/ejed.70008 · Cited by 52
A Social Perspective on AI in the Higher Education System: A Semisystematic Literature Review
Alshahrani, B. T., Pileggi, S. F., & Karimi, F. (2024) · Electronics · doi:10.3390/electronics13081572 · Cited by 82
ChatGPT in Education – Understanding the Bahraini Academics Perspective
Alrayes, A., Henari, T. F., & Ahmed, D. A. (2024) · The Electronic Journal of e-Learning · doi:10.34190/ejel.22.2.3250 · Cited by 48
“However, as identified by Holmes & Tuomi (2021) there is currently no regulations or official guidelines proposed internationally.”
BackgroundChatGPT and ELT: Exploring Teachers’ Voices
Mabuan, R. A. (2024) · International Journal of Technology in Education · doi:10.46328/ijte.523 · Cited by 75
“…Journal of Technology in Education (IJTE) 129 growing interest in exploring the potential of artificial intelligence (AI) and natural language processing (NLP) in language education (Alhalangy & AbdAlgane, 2023; Alqahtani et al., 2023; Holmes & Tuomi, 2022; Huang et al., 2023; Rizvi, 2023).”
BackgroundMakeleni, S., Mutongoza, B. H., & Linake, M. A. (2023) · Journal of Culture and Values in Education · doi:10.46303/jcve.2023.14 · Cited by 50
“Unlike humans, AI systems are built to process large quantities of data, learn from patterns, and make predictions based on such data – this usually happens through techniques such as machine learning, natural language processing, and robotics (Guilherme, 2019; Holmes & Tuomi, 2022).”
BackgroundDevelopment and validation of the perceived interactivity of learner-AI interaction scale
Wang, F., Cheung, A., Chai, C. S., & Liu, J. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12963-x · Cited by 50
“Based on the taxonomies of student-focused AI tools in education by Holmes and Tuomi (2022) and by Zhang and Aslan (2021), we modified and classified the tool into five categories: generative AI (e.g. ChatGPT and Midjourney), intelligent tutoring system (e.g. Spark and LAIX), automatic forma - tive…”
Durall, E., Hirvonen, N., Sharma, S., Hartikainen, H., Jylhä, V., Iivari, N., Kinnula, M., & Baizhanova, A. (2024) · Behaviour and Information Technology · doi:10.1080/0144929x.2024.2350666 · Cited by 41
“…approaches in technology education that uncritically assume the objectivity and neutrality of technology is questioned (Holmes et al. 2021; Holmes and Tuomi 2022; Ko et al. 2020; Vakil 2018) as this is shown limited to face the complex scenario created by the integration of AI in everyday…”
SDG 4, Academic Integrity and Artificial Intelligence: Clash or Win-Win Cooperation?
Artyukhov, A., Wołowiec, T., Artyukhova, N., Bogacki, S., & Vasylieva, T. (2024) · Sustainability · doi:10.3390/su16198483 · Cited by 42
Holmes, W. (2023) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00352-3 · Cited by 42
“…that we capitalise to highlight that it is a specific field of inquiry and development, and not simply a type of intelligence that is artificial" Holmes & Tuomi, 2022, p. 2) the word has long been identified as a problem (Crawford, 2021; Dreyfus et al., 1986; Tucker, 2022): “AI is not…”
BackgroundSelf-citationTeaching and AI in the postdigital age: Learning from teachers’ perspectives
Moylan, R., Code, J., & O'Brien, H. L. (2024) · Teaching and Teacher Education · doi:10.1016/j.tate.2024.104851 · Cited by 60
ChatGPT and the Educational AI Chatter: Full of Bullshit or Trying to Tell Us Something?
Costello, E. (2023) · Postdigital Science and Education · doi:10.1007/s42438-023-00398-5 · Cited by 48
Inclusive Education with AI: Supporting Special Needs and Tackling Language BarriersPreprint
Fitas, R. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5366398 · Cited by 41
Chatbots and other AI for learning: A survey of use and views among university students in Sweden
Malmström, H., Stöhr, C., & Ou, W. (2023) · Chalmers University of Technology · doi:10.17196/cls.csclhe/2023/01 · Cited by 37
Exploring the potential of AI-Chatbots in organic chemistry: An assessment of ChatGPT and Bard
Hallal, K., Hamdan, R., & Tlais, S. (2023) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2023.100170 · Cited by 62
Role of artificial intelligence in education: A conceptual review
Tahir, M. S., Hassan, F. D., & Shagoo, M. R. (2024) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.22.1.1217 · Cited by 54
“The ethical issues that can arise while using AI in education are the issues of transparency, fairness, privacy, morality, and removal of bias (Garrett, Beard, and Fiesler, 2020; Holmes and Tuomi, 2022; Kulet, Ilic, Dumangiu, Rankovic, Martins, Paun, and Mihoreanu, 2021).”
BackgroundRETRACTED: ChatGPT in the classroom: navigating the generative AI wave in management education
Leelavathi, R., & Surendhranatha, R. C. (2024) · Journal of Research in Innovative Teaching & Learning · doi:10.1108/jrit-01-2024-0017 · Cited by 54
RetractedJowarder, M. I. (2023) · Indonesian Journal of Innovation and Applied Sciences (IJIAS) · doi:10.47540/ijias.v3i2.878 · Cited by 36
Dubay, C., & Richards, M. B. (2024) · Marketing Education Review · doi:10.1080/10528008.2024.2411975 · Cited by 31
“These conversations equip students with the knowledge to ethically navigate AI’s challenges and apply their skills responsibly, enhancing their future readiness to employ technology for societal benefit (Boddu et al., 2022; Holmes & Tuomi, 2022).”
InfluentialUsing Large Language Models for Automated Grading of Student Writing about Science
Impey, C. D., Wenger, M. C., Garuda, N., Golchin, S., & Stamer, S. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-024-00453-7 · Cited by 57
“A complex typology of AI’s capabilities affects every aspect of education, from tutoring and assessment to the way institutions admit students and identify those who are at risk (Holmes & Tuomi, 2022).”
Generative AI and the future of connectivist learning in higher education
Liang, E. S., & Bai, S. (2025) · Journal of Asian Public Policy · doi:10.1080/17516234.2024.2386085 · Cited by 34
“…adoption and perceived readiness among educators (Ayanwale et al., 2022; Choi et al., 2021; Kohnke et al., 2023), the technical skills and knowledge required to use GenAI tools (Kohnke et al., 2023), and the perceived benefits and challenges of AI (Baidoo-Anu & Ansah, 2023; Holmes & Tuomi, 2022).”
Artificial intelligence in science education: A bibliometric review
Akhmadieva, R. S., Udina, N. N., Kosheleva, Y. P., Zhdanov, S. P., Timofeeva, M. O., & Budkevich, R. L. (2023) · Contemporary Educational Technology · doi:10.30935/cedtech/13587 · Cited by 54
Velli, K., & Zafiropoulos, K. (2024) · European Journal of Investigation in Health Psychology and Education · doi:10.3390/ejihpe14090169 · Cited by 36
“While AI streamlines mundane tasks and enhances efficiency, the irreplaceable role of teachers as supporters and facilitators remains essential for the holistic development of learners [12].”
BackgroundDecoding AI ethics from Users' lens in education: A systematic review
An, Q., Yang, J., Xu, X.-S., Zhang, Y.-F., & Zhang, H. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e39357 · Cited by 42
What Is AI-PACK? – Outline of AI Competencies for Teaching with DPACK
Lorenz, U., & Romeike, R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44900-0_2 · Cited by 36
Dickey, E., Bejarano, A., & Garg, C. (2024) · SN Computer Science · doi:10.1007/s42979-024-03074-y · Cited by 33
“Integrating AI tools into educational environments has garnered substantial attention, yielding invaluable insights and recommendations for its implementation [4, 5, 13, 17].”
Beyond ChatGPT: roles that artificial intelligence tools can play in an English language classroom
Tolstykh, O. M., & Oshchepkova, T. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00158-9 · Cited by 42
“Holmes and Tuomi [20] believe that rapid increase of AIEd systems could even stimulate new pedagogical approaches (p. 544).”
Avsheniuk, N., Lutsenko, O., Svyrydiuk, T., & Seminikhyna, N. (2024) · Arab World English Journal · doi:10.24093/awej/chatgpt.14 · Cited by 31
Yu, J. H., Chauhan, D., Iqbal, R. A., & Yeoh, E. (2024) · Educational Technology Research and Development · doi:10.1007/s11423-024-10425-2 · Cited by 43
“Holmes and Tuomi (2022) developed a typology of AI systems in education, offering a structured understanding of different AI applications.”
Students’ Intention toward Artificial Intelligence in the Context of Digital Transformation
Milićević, N., Kalaš, B., Djokić, N., Malčić, B. D., & Djokic, I. (2024) · Sustainability · doi:10.3390/su16093554 · Cited by 54
Rethinking responsible AI from ethical pillars to sociotechnical practice
Ibitoye, A., Nkwo, M. S., & Orji, R. (2025) · AI and Ethics · doi:10.1007/s43681-025-00809-2 · Cited by 48
“…across critical sectors such as healthcare [ 1, 2 ], finance [ 3], agriculture [ 4 ], business [ 5], environmental sustainability [ 6 ], education [ 7], governance or through super intelligent innovations [ 8 ] has intensified calls for “Responsible AI”, a normative vision grounded in principles…”
Beyond Mastery: Toward a Broader Understanding of AI in Education
Tuomi, I. (2023) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00343-4 · Cited by 45
“…and learning objectives are now extended towards social and emotional skills and the new requirements of the 21st century knowledge society, the ghost of Bloom’s (1984) personalized tutors still looms large over the AIED community, commercial spin-offs, and policy debates (Holmes & Tuomi, 2022).”
Self-citationAI in teaching English as a foreign language: Effectiveness and prospects in Kazakh higher education
Kemelbekova, Z., Degtyareva, X., Yessenaman, S., Ismailova, D., & Seidaliyeva, G. (2024) · XLinguae · doi:10.18355/xl.2024.17.01.05 · Cited by 25
AI-Based Education Tools for Enabling Inclusive Education: Challenges and Benefits
Šumak, B., López–de–Ipiña, D., Dziabenko, O., Correia, S. D., Carvalho, L., Lopes, S., Şimşek, İ., Can, T., Kline, D. I., & Pušnik, M. (2024) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro60963.2024.10569714 · Cited by 28
“Tools such as Smart Sparrow, promote learner interaction with educational content [17].”
BackgroundAl‐Mamary, Y. H., Alfalah, A. A., Shamsuddin, A., & Abubakar, A. A. (2024) · Journal of Applied Research in Higher Education · doi:10.1108/jarhe-04-2024-0179 · Cited by 49
Beyond the “Wow” Factor: Using Generative AI for Increasing Generative Sense-Making
Makransky, G., Shiwalia, B. M., Herlau, T., & Blurton, S. (2025) · Educational Psychology Review · doi:10.1007/s10648-025-10039-x · Cited by 27
“According to a taxonomy developed by Holmes and Tuomi (2022), AIEd applications can be classified into those that are student-focused, teacher-focused, or institution-focused.”
InfluentialLarge language models for sustainable assessment and feedback in higher education
Agostini, D., & Picasso, F. (2024) · Intelligenza Artificiale · doi:10.3233/ia-240033 · Cited by 41
“Simultaneously, teachers and administrators are required to have clear perspectives on the potential of AI in education and, eventually, to incorporate this ground-breaking technology into their practice[21].”
Altınay, Z., Altınay, F., Sharma, R. C., Dağlı, G., Shadiev, R., Yıkıcı, B., & Altınay, M. (2024) · Societies · doi:10.3390/soc14080148 · Cited by 27
Using AI to Support Education for Collective Intelligence
Casebourne, I., Shi, S., Hogan, M., Holmes, W., Hoel, T., Wegerif, R., & Yuan, L. Y. (2024) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-024-00437-7 · Cited by 38
“Accordingly, AI is perhaps better defined as computer systems that interact with the world through capabilities that we usually think of as human (Holmes & Tuomi, 2022).”
Self-citationMeasuring secondary education students' self-regulated learning processes with digital trace data
Lämsä, J., de Mooij, S. M. M., Aksela, O., Athavale, S., Bistolfi, I., Azevedo, R., Bannert, M., Gašević, D., Molenaar, I., & Järvelä, S. (2025) · Learning and Individual Differences · doi:10.1016/j.lindif.2024.102625 · Cited by 41
Artificial intelligence as a teaching tool in university education
Marín, Y. R., Caro, O. C., Rubio, Y. D. C. M., Tuesta, J. N. A., Bardales, E. S., Rituay, A. M. C., & Santos, R. C. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1578451 · Cited by 53
AI in Inclusive Education: A Systematic Review of Opportunities and Challenges in the Indian Context
Gupta, M., & Kaul, S. (2024) · MIER Journal of Educational Studies Trends & Practices · doi:10.52634/mier/2024/v14/i2/2702 · Cited by 34
“This sometimes prioritises the corporate bottom line over the child’s genuine educational and cognitive development (Holmes & Tuomi, 2022).”
InfluentialMambile, C., & Mwogosi, A. (2024) · Technological Sustainability · doi:10.1108/techs-03-2024-0014 · Cited by 42
Engeness, I., & Gamlem, S. M. (2025) · Integrative Psychological and Behavioral Science · doi:10.1007/s12124-025-09894-8 · Cited by 26
“…machine learning have significantly altered the educational landscape; they are widely regarded as a turning point for educational methodologies (Holmes & Tuomi, 2022) and are identified as revolutionary tools that indicate a paradigm shift in education (Gentile et al., 2023; Guan et al., 2020;…”
Chea, P., & Xiao, Y. (2024) · Journal of General Education and Humanities · doi:10.58421/gehu.v3i3.242 · Cited by 49
“Many current commercial AI systems developed for education, such as Intelligent Tutoring Systems (ITS), focus on providing automated, adaptive, and individualized instruction, offering students a personalized and adaptive learning experience [33].”
BackgroundNour, A. H., Mokaizh, A. A. B., Alazaiza, M. Y. D., Bashir, M. J. K., Mustafa, S. E., & Baarimah, A. O. (2024) · Sustainability · doi:10.3390/su162310565 · Cited by 28
Alwakid, W. N., Dahri, N. A., Humayun, M., & Alwakid, G. N. (2025) · Systems · doi:10.3390/systems13070517 · Cited by 62
Empowering Student Learning Through Artificial Intelligence: A Bibliometric Analysis
Yun, G., Lee, K. M., & Choi, H. H. (2024) · Journal of Educational Computing Research · doi:10.1177/07356331241278636 · Cited by 26
“For instance, data-driven AI, commonly recognized as machine learning, enables both students and educators to solve problems and generate output that is tailored to speci fi c needs by harnessing data (Holmes & Tuomi, 2022).”
Writing without borders: AI and cross-cultural convergence in academic writing quality
Prakash, A., Aggarwal, S., Varghese, J. J., & Varghese, J. J. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-05484-6 · Cited by 35
“Arti fi cial Intelligence (AI), described as one of the most transformative innovations since the Palaeolithic era and even referred to as the “ new oil ” (Holmes & Tuomi, 2022), has signi fi cantly transformed academic writing practices.”
High School Teachers’ Adoption of Generative AI
Elstad, E., & Eriksen, H. (2024) · Nordic Journal of Comparative and International Education (NJCIE) · doi:10.7577/njcie.5736 · Cited by 28
“Overviews of research on educational AI have taken several directions (AlBadarin et al., 2023; Bahroun et al., 2023; Baytak, 2023; Celik et al., 2022; Holmes & Tuomi, 2022; Hopfenbeck et al., 2023; Kuhail et al., 2023; Lo, 2023).”
BackgroundOn the relation of causality- versus correlation-based feature selection on model fairness
Saarela, M. (2024) · ACM Symposium on Applied Computing · doi:10.1145/3605098.3636018 · Cited by 22
“…learning systems can provide instructions in mixed-ability learning groups, chatbots can provide students with detailed and timely feedback on their writing products, and automated assessments can free teachers from some repetitive work and give them more room to support their students [21, 47].”
Background
Metadata
- Authors
- Holmes, W., & Tuomi, I.
- Affiliations
- University College London (GB)
- Meaning Processing (Finland) (FI)
- Corresponding author
- Holmes, Wayne
- Venue
- European Journal of Education
- Publisher
- Wiley
- Published
- 2022-10-30
- Volume
- 57
- Issue
- 4
- Pages
- 542-570
- ISSN
- 0141-8211 (ISSN-L)
- 1465-3435
- OpenAlex
- W4307888124
- Semantic Scholar
- bdfcc87264808446152972591e66777c0c69f837
- Cited by
- 1,093
- References
- 111
- Citations by year
- 2026: 470 · 2025: 343 · 2024: 212 · 2023: 66 · 2022: 2
- FWCI
- 154.40
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- European Journal of Education (publishedVersion, cc-by)
- UCL Discovery (University College London) (submittedVersion)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence in education, pedagogical assumptions, educational assumptions, functions of education
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