The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index; none of the 53 sources its claims cite is checked for support yet.
Editorial: Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges
Open accessAbstract
The proliferation of artificial intelligence (AI) technologies and chatbots has the potential to significantly reshape higher education. It is now imperative for stakeholders in this sector to grasp the fundamental aspects of AI technologies and understand their implications. This paper not only introduces basic AI concepts but also explains their specific applications and relevance in the higher education context. Moreover, it outlines the prospects of using AI technologies and chatbots to boost student engagement, presenting a synthesis of the opportunities available. Concurrently, we discuss the concerns and challenges associated with integrating AI into higher education settings. Several articles included in this special issue explore these opportunities and challenges from diverse viewpoints and within various contexts, across countries such as Australia, the United Kingdom, Vietnam, Cyprus, and GCC nations. Finally, we propose several avenues for future research aimed at enhancing student engagement through AI, charting a path forward for empirical evidence and practical application of AI and chatbots in enhancing student engagement.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This editorial introduces a JUTLP special issue on enhancing student engagement through AI and chatbots, explaining basic AI/ML/deep learning and generative AI concepts in higher education. It synthesizes opportunities (intelligent tutoring, personalised learning, accessibility) and challenges (data privacy, algorithmic bias, academic integrity) and proposes future research directions.
Key findings
As an editorial, it synthesizes rather than reports primary results:
- AI, ML, and DL are nested concepts; ML applied to early engagement data can identify students at risk of failing "as early as the fourth week of the Fall semester" (citing Gray & Perkins, 2019).
- GenAI/LLMs can serve as teaching assistants, generate personalised content and feedback, and support accessibility (e.g., auto-generated captions, real-time translation), but full personalisation requires deeper integration of educational theory.
- Key concerns: data privacy, algorithmic bias disadvantaging students by race, gender, or socioeconomic status, and threats to academic integrity and critical thinking from over-reliance on AI-generated content.
- The special issue comprises "a commentary and seven articles" spanning Australia, the United Kingdom, Vietnam, Cyprus, and GCC nations.
Methods
Not stated in the paper (editorial synthesis; no empirical design, sample, or analysis reported). It reviews prior literature and summarises the special issue's contributions.
Limitations
Not stated in the paper. Evident from the text: it is a conceptual editorial without primary data, and its claims about AI's benefits and risks are drawn from cited secondary sources rather than new evidence; the proposed research directions (adaptive systems, ethical AI, collaborative learning, longitudinal studies) remain unaddressed by the paper itself.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
44 Own interpretation · 33 Not checked yet
None of the sources cited for this claim have been checked yet.
In the paper
“While these technologies offer great promise, educators and institutions will need to overcome significant obstacles for successful implementation (Crawford, Cowling, & Allen, 2023;Perkins, 2023).”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Introduction
What the sources say
- Not checked yetcites Crawford, Cowling, & Allen, 2023
- Not checked yetcites Perkins, 2023
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“The proliferation of artificial intelligence (AI) technologies and chatbots has the potential to significantly reshape higher education.”
- Also stated in: § Introduction · boosted
“Despite these hurdles, the disruptive potential of emerging technologies like Artificial Intelligence (AI) is undeniable (Crawford, Cowling, & Allen, 2023;Kelly et al., 2023;Lodge et al., 2023), offering new horizons for learning and teaching practices.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Abstract
What the sources say
- Not checked yetcites Crawford, Cowling, & Allen, 2023
- Not checked yetcites Kelly et al., 2023
- Not checked yetcites Lodge et al., 2023
boostedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
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- Also stated in: § Introduction · boosted
None of the sources cited for this claim have been checked yet.
In the paper
“The application of deep learning in education is particularly promising for analysing learners' cognitive and emotional processes, with studies by Nguyen et al. (2022) and Tzirakis et al. (2017) highlighting its potential.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > AI, Machine Learning (ML), and Deep Learning (DL)
What the sources say
- Not checked yetcites Nguyen et al. (2022)
- Not checked yetcites Tzirakis et al. (2017)
plainconditional- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Moreover, machine learning algorithms can dynamically adjust the learning environment in real-time, providing each student with a customised learning trajectory that optimally supports their progress and engagement (Ninaus et al., 2019;Raj & Renumol, 2022).”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > AI, Machine Learning (ML), and Deep Learning (DL)
What the sources say
- Not checked yetcites Ninaus et al., 2019
- Not checked yetcites Raj & Renumol, 2022
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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In the paper
“For instance, Gray and Perkins (2019) demonstrated that by applying machine learning techniques to early learning engagement data, it is feasible to identify students at risk of failing as early as the fourth week of the Fall semester.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > AI, Machine Learning (ML), and Deep Learning (DL)
What the sources say
- Not checked yetcites Gray and Perkins (2019)
boosteddirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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In the paper
“In the educational sphere, AI systems can significantly enhance learning and teaching processes (Järvelä et al., 2023).”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > AI, Machine Learning (ML), and Deep Learning (DL)
What the sources say
- Not checked yetcites Järvelä et al., 2023
plainconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“This research provides insights into how AI tools utilising LLMs are being integrated into the writing process, highlighting the ways in which these technologies complement human skills to enhance productivity, creativity, and efficiency in academic writing tasks.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Generative AI (GenAI) and (Multimodal-) Large Language Models (M-LLMs)
What the sources say
- Not checked yetcites Nguyen et al. (2024)
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“This limited uptake could be attributed to several challenges, including issues related to technological accessibility, the digital divide, and pedagogical integration (Nguyen, 2022;Nguyen, Hong, et al., 2020;Strobl et al., 2019).”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Interactive Teaching Aids
What the sources say
- Not checked yetcites Nguyen, 2022
- Not checked yetcites Nguyen, Hong, et al., 2020
- Not checked yetcites Strobl et al., 2019
hedgedconditional- 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
“These factors collectively hinder the broader adoption of ITS in formal education.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Interactive Teaching Aids
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
“Technological accessibility concerns arise from the substantial resources required to implement and maintain ITS, which can be a barrier in under-resourced educational environments.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Interactive Teaching Aids
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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- Probably not a substantial claim. 33
- It may state two separate claims as one. 27
None of the sources cited for this claim have been checked yet.
In the paper
“The invasion of digital technology into higher education, spanning several decades, has been met with both enthusiasm and skepticism, characterised by challenges such as user resistance, lack of necessary skills, policy complications, and ethical and privacy concerns (Essien et al., 2024;Nguyen, Gardner, et al., 2020).”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Introduction
What the sources say
- Not checked yetcites Essien et al., 2024
- Not checked yetcites Nguyen, Gardner, et al., 2020
plaincorrelational- Asserted: passes — The authors put it forward as true.
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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
“This requires strategies that bridge skill gaps, carefully consider ethical implications, and ensure alignment with existing policies (Nguyen et al., 2023).”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Introduction
What the sources say
- Not checked yetcites Nguyen et al., 2023
plain- Asserted: passes — The authors put it forward as true.
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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
“They might include intelligent tutoring systems (ITS), adaptive learning systems, or personalised learning platforms, which can customize instruction, provide timely feedback to learners, and aid in developing adaptive learning resources tailored to individual student needs.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > AI, Machine Learning (ML), and Deep Learning (DL)
hedgeddirect- Asserted: passes — The authors put it forward as true.
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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
“In educational environments, this type of AI can be used to create personalised learning resources, model real-life situations for hands-on learning, and develop detailed simulations or models for scientific investigation.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Generative AI (GenAI) and (Multimodal-) Large Language Models (M-LLMs)
plain- Asserted: passes — The authors put it forward as true.
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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
“For instance, generative AI can formulate mathematical or physics practice questions, construct historical recreations, or develop language exercises that adjust to a student's level of proficiency.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Generative AI (GenAI) and (Multimodal-) Large Language Models (M-LLMs)
plaindirect- Asserted: passes — The authors put it forward as true.
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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, multimodal deep learning approaches have been explored for predicting intricate learning activities, such as those involved in shared regulation during collaborative learning scenarios.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > AI, Machine Learning (ML), and Deep Learning (DL)
What the sources say
- Not checked yetcites Nguyen et al. (2022)
plaincorrelational- Asserted: passes — The authors put it forward as true.
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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
“These technological advancements in AI, machine learning, and deep learning open new horizons for personalised and adaptive learning experiences in education, offering the potential to deeply understand and enhance the learning process based on individual learner data and interactions.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > AI, Machine Learning (ML), and Deep Learning (DL)
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
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
“Generative AI (GenAI) and (Multimodal-)Large Language Models (M-LLMs) represent significant advancements in the field of artificial intelligence, offering profound implications for learning and teaching.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Generative AI (GenAI) and (Multimodal-) Large Language Models (M-LLMs)
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. 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 recent advancements in AI, particularly with generative AI, emphasise the transformative impact on higher education, raising critical questions about the future of learning and teaching with AI technologies.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Introduction
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
“These technologies have the potential to revolutionise educational practices by providing innovative tools for content creation, personalised learning, and interactive engagement.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Generative AI (GenAI) and (Multimodal-) Large Language Models (M-LLMs)
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“Furthermore, the comprehensive review by Zawacki-Richter et al. (2019) emphasised AI's potential in facilitating adaptive learning systems, personalisation, and intelligent tutoring systems, underscoring the technology's capability to cater to diverse learning needs and styles.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Introduction
What the sources say
- Not checked yetcites Zawacki-Richter et al. (2019)
plainconditional- 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
“These models can comprehend and produce text in ways that are contextually relevant and syntactically coherent, making them particularly useful for a variety of educational applications.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Generative AI (GenAI) and (Multimodal-) Large Language Models (M-LLMs)
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“However, the deployment of AI in educational contexts also introduces ethical concerns, including data privacy, bias in AI algorithms, and the implications of AI-mediated interactions on the student learning experience (Essien et al., 2024).”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Introduction
What the sources say
- Not checked yetcites Essien et al., 2024
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Nguyen et al. (2020) highlighted the capacity of AI to unveil critical indicators previously overlooked by stakeholders at various levels of the educational hierarchy, suggesting a transformative role for AI in shaping strategic decisions and educational outcomes.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Introduction
What the sources say
- Not checked yetcites Nguyen et al. (2020)
hedgedconditional- 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
“These advancements demonstrate the potential for a revolutionary change in education, creating learner-centred environments that could significantly boost student engagement.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Introduction
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
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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
“LLMs can support personalised learning by offering tutoring or feedback on written assignments, generating practice essay prompts, and facilitating language learning through conversation simulations.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Generative AI (GenAI) and (Multimodal-) Large Language Models (M-LLMs)
plainconditional- 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
“Within the context of education, machine learning can be harnessed for learning analytics to analyse vast amounts of student data, offering personalised instruction recommendations, identifying trends in student behaviour, and forecasting academic performance.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > AI, Machine Learning (ML), and Deep Learning (DL)
plainconditional- 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
“This technology allows educators to offer varied, engaging, and customised learning experiences.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Generative AI (GenAI) and (Multimodal-) Large Language Models (M-LLMs)
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“The incorporation of AI technology in higher education to enhance student engagement offers immersive opportunities but also presents several challenges that need to be addressed.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Opportunities in Enhancing Student Engagement through AI in Higher Education
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
“MLLMs have the potential to transform educational content delivery by making it more interactive and engaging.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Generative AI (GenAI) and (Multimodal-) Large Language Models (M-LLMs)
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. 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
“For example, an MLLM can analyse a scientific text and generate corresponding visual aids, such as diagrams or animations, to help students better understand complex concepts.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Generative AI (GenAI) and (Multimodal-) Large Language Models (M-LLMs)
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
“This method not only increases the accuracy of educational research but also enhances the quality of the learning experiences provided.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Generative AI (GenAI) and (Multimodal-) Large Language Models (M-LLMs)
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Additionally, LLMs can assist in the creation of instructional content, summarise academic texts, and even help in drafting research papers or educational resources, thereby reducing the workload on educators and enabling more focused and effective teaching strategies (Dwivedi et al., 2023;Nguyen et al., 2024).”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Generative AI (GenAI) and (Multimodal-) Large Language Models (M-LLMs)
What the sources say
- Not checked yetcites Dwivedi et al., 2023
- Not checked yetcites Nguyen et al., 2024
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“These systems can assess student submissions, identify errors, and provide targeted feedback to help students improve (Mousavinasab et al., 2021;Tchounikine et al., 2010).”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Interactive Teaching Aids
What the sources say
- Not checked yetcites Mousavinasab et al., 2021
- Not checked yetcites Tchounikine et al., 2010
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
“This multimodal approach caters to visual learners and can aid in the retention of information by providing multiple reference points for understanding material.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Generative AI (GenAI) and (Multimodal-) Large Language Models (M-LLMs)
plainconditional- Asserted: passes — The authors put it forward as true.
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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
“By offering support that is tailored to the student's current level of understanding, ITS can enhance student engagement and lead to learning outcomes (VanLehn, 2011).”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Interactive Teaching Aids
What the sources say
- Not checked yetcites VanLehn, 2011
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.
It may state two separate claims as one.
In the paper
“A recent study by Nguyen et al. (2024) has demonstrated the patterns of collaboration between humans and AI in AI-assisted academic writing.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Generative AI (GenAI) and (Multimodal-) Large Language Models (M-LLMs)
What the sources say
- Not checked yetcites Nguyen et al. (2024)
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: 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
“By generating personalised content, assessments, and feedback dynamically, GenAI supports a variety of learning styles and schedules, thereby increasing engagement and making education more responsive to the diverse needs and lifestyles of students.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Personalised Learning Experience and Accessibility
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
“The integration of AI, particularly Generative AI, into education brings substantial benefits but also raises significant ethical issues and challenges to academic integrity that need to be thoughtfully examined and managed (Crawford, Cowling, & Allen, 2023;Holmes et al., 2021;Nguyen et al., 2023).”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Concerns and Challenges of Implementing AI in Higher Education
What the sources say
- Not checked yetcites Crawford, Cowling, & Allen, 2023
- Not checked yetcites Holmes et al., 2021
- Not checked yetcites Nguyen et al., 2023
plaincorrelational- 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
“The recent availability of GenAI technologies has further propelled the personalised learning experience by facilitating real-time interactions with each learner.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Personalised Learning Experience and Accessibility
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.
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, these resources are often static and do not truly cater to the unique needs of each learner.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Personalised Learning Experience and Accessibility
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.
None of the sources cited for this claim have been checked yet.
In the paper
“While personalised learning has been acknowledged as an effective method that customises educational experiences to meet the specific needs of individual learners (Xie et al., 2019;Zheng et al., 2021), the significant resources needed to put this model into practice make it challenging to adopt on a large scale (Nguyen, Gardner, et al., 2020).”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Personalised Learning Experience and Accessibility
What the sources say
- Not checked yetcites Xie et al., 2019
- Not checked yetcites Zheng 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.
None of the sources cited for this claim have been checked yet.
In the paper
“Numerous studies have explored the development of personalised learning, resulting in various prototypes that aim to enhance its feasibility and effectiveness (Denny et al., 2015;Hsieh et al., 2012).”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Personalised Learning Experience and Accessibility
What the sources say
- Not checked yetcites Denny et al., 2015
- Not checked yetcites Hsieh et al., 2012
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.
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
“Moreover, GenAI can assist in evaluating students' work by examining written assignments, presentations, and spoken responses, providing comprehensive feedback that covers both the textual and auditory elements of the content.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Interactive Teaching Aids
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
“This accessibility allows for broader integration into various educational settings, supporting a more dynamic and responsive learning environment.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Interactive Teaching Aids
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.
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 instance, AI-powered tools can automatically generate captions for lecture videos or provide realtime language translation, removing barriers to access and engagement for a broader spectrum of students.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Personalised Learning Experience and Accessibility
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.
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
“Thus, generative AI is transforming the educational field, making it more interactive and adaptable to the needs of today's learners.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Interactive Teaching Aids
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. 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
“GenAI can serve as dynamic teaching assistants, offering educators a range of tools to enhance their teaching methods.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Interactive Teaching Aids
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.
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 capability of GenAI to provide instantaneous feedback and tailor learning materials according to individual student needs represents a transformative step towards true personalised learning.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Personalised Learning Experience and Accessibility
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: 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 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
“Moreover, GenAI enhances educational accessibility and convenience, allowing students to interact with learning materials anytime and anywhere.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Personalised Learning Experience and Accessibility
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.
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 round-the-clock availability of tailored educational resources through GenAI not only makes learning more accessible but also substantially enriches the overall learning experience.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Personalised Learning Experience and Accessibility
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: 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 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
“Teachers can use GenAI to create diverse educational materials that incorporate text, visuals, and audio, making lessons more dynamic and engaging.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Interactive Teaching Aids
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.
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 flexibility is particularly beneficial for learners who need to balance educational pursuits with other commitments or those who prefer studying outside conventional classroom settings.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Personalised Learning Experience and Accessibility
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.
In the paper
“Institutional leadership plays a crucial role in ensuring the ethical deployment and management of ChatGPT by establishing standards and practices that prioritize transparency, accountability, and the protection of user data (Crawford, Cowling, & Allen, 2023).”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Concerns and Challenges of Implementing AI in Higher Education
What the sources say
- Not checked yetcites Crawford, Cowling, & Allen, 2023
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: 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
“This reliance on AI tools also raises concerns about students' development of critical thinking and problem-solving skills, as the ease of access to information might deter deeper engagement with learning materials.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Concerns and Challenges of Implementing AI in Higher Education
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.
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 of the foremost ethical concerns involves data privacy.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Concerns and Challenges of Implementing AI in Higher Education
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
“As students increasingly rely on AI-generated content, there is a risk that it could facilitate academic dishonesty, making it difficult to ascertain whether work submitted is truly indicative of a student's own understanding and effort.”
§ Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges > Concerns and Challenges of Implementing AI in Higher Education
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.
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
“The literature highlights that many educators appreciate GenAI’s potential for enhancing efficiency and streamlining processes [55].”
From Generative AI in Higher Education: Balancing Innovation and Integrity (2025) · cites this paper as [55]
Experimental — accuracy not yet measuredIn the citing paper
“the reliability and accuracy of AI models raise ethical and epistemological concerns that impact the development of critical skills, as students may internalize information without a rigorous assessment process”
From Determining Factors for the Development of Critical Thinking in Higher Education (2025) · cites this paper as Nguyen et al. 2024
Citations
Cites
ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · doi:10.35542/osf.io/5er8f · Cited by 7,552
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326 · cited 1×
“Moreover, the integration of AI technologies in educational settings also poses challenges to academic integrity (Kasneci et al., 2023; Kishore et al., 2023; Nguyen et al., 2024).”
BackgroundZawacki-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 · cited 1×
“Furthermore, the comprehensive review by Zawacki-Richter et al. (2019) emphasised AI's potential in facilitating adaptive learning systems, personalisation, and intelligent tutoring systems, underscoring the technology's capability to cater to diverse learning needs and styles.”
BackgroundDwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584
Ethics of AI in Education: Towards a Community-Wide Framework
Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., et al. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00239-1 · Cited by 1,437
Ethical principles for artificial intelligence in education
Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B.-P. T. (2023) · Education and Information Technologies · doi:10.1007/s10639-022-11316-w · Cited by 1,228 · cited 5×
“…integration of AI, particularly Generative AI, into education brings substantial benefits but also raises significant ethical issues and challenges to academic integrity that need to be thoughtfully examined and managed (Crawford, Cowling, & Allen, 2023; Holmes et al., 2021; Nguyen et al., 2023).”
BackgroundInfluentialSelf-citationBaker, R. S., & Hawn, A. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00285-9 · Cited by 993
Perkins, M. (2023) · Journal of University Teaching and Learning Practice · doi:10.53761/1.20.02.07 · Cited by 839
End-to-End Multimodal Emotion Recognition using Deep Neural Networks
Tzirakis, P., Trigeorgis, G., Nicolaou, M. A., Schuller, B., & Zafeiriou, S. (2017) · arXiv · doi:10.48550/arxiv.1704.08619 · Cited by 752
Xie, H., Chu, H.-C., Hwang, G.-J., & Wang, C.-C. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.103599 · Cited by 713
Research works printed but not linked: 2
- Nguyen, A., Järvelä, S., Wang, Y., & Róse, C. (2022). Exploring Socially Shared Regulation with an AI Deep Learning Approach Using Multimodal Data. Proceedings of International Conferences of Learning Sciences (ICLS). https://2022.isls.org/proceedings/
- Whitehead, R., Nguyen, A., & Järvelä, S. (2024). The Generative Multimodal Analysis (GMA) Methodology for Studying Socially Shared Regulation in Collaborative Learning. The International Conference on Learning Analytics & Knowledge (LAK24). https://www.researchgate.net/profile/Ridwan- Whitehead/publication/378964778_The_Generative_Multimodal_Analysis_GMA_Method ology_for_Studying_Socially_Shared_Regulation_in_Collaborative_Learning/links/65f34f 211f0aec67e28fd7d9/The-Generative-Multimodal-Analysis-GMA-Methodology-for- Studying-Socially-Shared-Regulation-in-Collaborative-Learning.pdf
Cited by
Sorted by citations
Generative AI in Higher Education: Balancing Innovation and Integrity
Francis, N. J., Jones, S., & Smith, D. P. (2025) · British Journal of Biomedical Science · doi:10.3389/bjbs.2024.14048 · Cited by 295
“The literature highlights that many educators appreciate GenAI ’ s potential for enhancing ef fi ciency and streamlining processes [55].”
Dang, B., Huynh, T.-N., Gul, F., Rosé, C. P., Järvelä, S., & Nguyen, A. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13607 · Cited by 103
Self-citationMastering knowledge: the impact of generative AI on student learning outcomes
Pallant, J. L., Blijlevens, J., Campbell, A., & Jopp, R. (2026) · Studies in Higher Education · doi:10.1080/03075079.2025.2487570 · Cited by 141
Study on the Impact of Artificial Intelligence on Student Learning Outcomes
Sasikala, P., & Ravichandran, R. (2024) · Journal of Digital Learning and Education · doi:10.52562/jdle.v4i2.1234 · Cited by 76
Generative artificial intelligence and sustainable higher education: Mapping the potential
Νικολοπούλου, Κ. (2025) · Journal of Digital Educational Technology · doi:10.30935/jdet/15860 · Cited by 76
Metadata
- Authors
- Nguyen, A., Kremantzis, M., Essien, A., Petrounias, I., & Hosseini, S.
- Affiliations
- University of Oulu (FI)
- University of Bristol (GB)
- Athens University of Economics and Business (GR)
- Tecnológico de Monterrey (MX)
- Venue
- Journal of University Teaching and Learning Practice
- Publisher
- Open Access Publishing Association
- Published
- 2024-04-19
- Volume
- 21
- Issue
- 06
- ISSN
- 1449-9789 (ISSN-L)
- OpenAlex
- W4394949418
- Semantic Scholar
- 6f83affb4051438cf2b321cf8c15a2197a008199
- Cited by
- 146
- References
- 32
- Citations by year
- 2026: 58 · 2025: 65 · 2024: 23
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Journal of University Teaching and Learning Practice (publishedVersion, cc-by-nd)
- Explore Bristol Research (submittedVersion, cc-by-nd)
- Bristol Research (University of Bristol) (submittedVersion, cc-by-nd)
- Funders
- Oulun Yliopisto (352788)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence in higher education, student engagement, chatbots, artificial intelligence in education, AI implementation challenges
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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