The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index; 39 of the 69 sources its claims cite were checked for support.
A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment
Open accessAbstract
Introduction: The emergence of generative artificial intelligence (AI) presents many opportunities and challenges to teaching and learning in higher education. However, compared to student- or administration-facing AI, little attention has been given to the impact of AI on faculty's perspective or their curriculum, instruction, and assessment (CIA) practices. Methods: To address this gap, we conducted a systematic review of articles published within the first nine months following the release of ChatGPT. After screening following PRISMA statement guidelines, our review yielded 33 studies that met the inclusion criteria. Results: Most of these studies (n = 17) were conducted in Asia, and simulation and modeling were the most frequently used methods (n = 15). Thematic analysis of the studies resulted in four themes about the impact of AI on CIA triad: (a) generation of new material, (b) reduction of staff workload, (c) automation/optimization of evaluation, and (d) challenges for CIA. Discussion: Overall, this review informs the promising contribution of AI to higher education CIA practices as well as the potential challenges and problems it introduces. Implications for future research and practices are proposed.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This systematic review synthesizes 33 empirical studies published in the first nine months after ChatGPT’s release on how AI affects higher education curriculum, instruction, and assessment (CIA). It finds early benefits in material generation, workload reduction, and assessment automation, alongside challenges around academic integrity, ethics, training, and curriculum integration.
Key findings
- The review included 33 studies. Asia accounted for 17 of 33, predominantly China; Europe had 8, North America 5, Latin America 3, Middle East 2, and Australia 2. Modeling/simulation was the most frequent method (15), followed by experiment (7), interview (7), and survey (6).
- Thematic analysis yielded four themes: (a) generation of new material, (b) reduction of staff workload, (c) automation/optimization of evaluation, and (d) challenges for CIA. Instruction was addressed in 21 studies (64%), assessment in 17 (52%), and curriculum in 9 (27%); only 11 studies integrated two or more CIA aspects, and only three attempted all three.
- Reported benefits include AI-generated curriculum content and immersive learning environments, personalized instruction (e.g., teaching effect increased by 9.44% in one embedded-glasses study; piano teaching efficacy increased by 7.31%), workload reduction via virtual assistants, and automated assessment (e.g., one model correctly assigned 85% of students to at-risk/leader clusters; another rated teaching quality almost 7/10).
- Challenges include uncertainty about integrating AI into curricula, concerns about instructors’ AI competencies and ethics, inadequate technical support/training, and assessment threats such as AI-assisted cheating and difficulty detecting AI-generated work.
Methods
- Systematic review following PRISMA; searched Scopus, Web of Science, and EBSCOhost for peer-reviewed English journal articles published December 2022–August 2023. The search yielded 2,810 records; after deduplication, 279 were screened; 33 articles met inclusion criteria.
- Inclusion required empirical analysis of AI’s relationship to one or more of higher education curriculum, instruction, or assessment; excluded non-HE contexts, non-empirical work, and broad AI perspectives.
- Inductive thematic analysis (Braun & Clarke, 2006) produced 25 initial descriptive codes, merged into four themes; the first author extracted and coded data, and other authors reviewed coding and themes.
Limitations
- Paper states: the review was limited to English-language articles, so generalizability to non-English contexts is cautioned; it was limited to the first 9 months after ChatGPT’s release (30 November 2022), making it preliminary and likely already superseded.
- Paper-stated weaknesses of the reviewed literature: research is narrow and largely from WEIRD societies, with developing countries poorly represented; little research on real-world HE classroom pedagogical activities, course development, and assessment design; and a lack of crossdisciplinary collaboration between computer and social scientists.
- Evident in the text: data extraction and coding were led by the first author, with other authors reviewing, and no independent duplicate coding of all included articles is described.
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Claims the paper makes
21 Not checked yet · 20 Partly supported · 18 Own interpretation · 18 Own result · 15 No full text of the sources · 13 Supported · 12 No basis given · 5 Sources not found · 5 Own method · 1 Mixed support · 1 Not supported · accuracy not yet measured
Some cited sources support this claim and some don’t — read each source’s verdict below.
In the paper
“Ten studies have demonstrated that AI could support staff in curriculum, instruction, and assessment, by reducing their logistical workloads, especially in terms of labor related to curriculum design, interactions with students, delivering personalized instruction, and preparing adapted or personalized assignments (e.g., Holmes et al., 2023;Pereira et al., 2023;Sajja et al., 2023;Devi and Rroy, 2”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Reduction of staff workload
What the sources say
- Disputedcites Holmes et al., 2023 sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 0 partially supported, 2 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Checked by a reasoning model — may be wrong.
- Not checked — no full text of the sourcecites Pereira et al., 2023 source
- Partly supportedcites Sajja et al., 2023 sourceExperimental — accuracy not yet measured
The Abstract and Introduction describe a course-specific assistant that answers students’ curriculum and logistics questions and is intended to reduce instructors’ and TAs’ logistical workload. They do not show that it designs curricula, delivers personalized instruction, prepares adapted assignments, or reduces assessment work; the workload benefit is presented as envisioned rather than demonstrated. (LLM, conf=0.86)
boostedconditional- 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.
- Disputed
Every source the paper cites for this claim was checked and supports it.
In the paper
“According to Pisica et al. (2023), 18 academics from Romanian universities reported the benefits of AI in curriculum, which included generating new content for existing courses and developing new curricula or disciplines.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Generate new curriculum content
What the sources say
- Supportedcites Pisica et al. (2023) source
The Research Methodology section states that 18 academics from five Romanian universities were interviewed. Section 4.1 describes new curricula, new content for existing curricula, and new disciplines as benefits of AI identified in the academics’ views. (LLM, conf=0.94)
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.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“AI technology, such as smart classroom, enables the simulation of the atmosphere of a “real” classroom, practicum, or internship, in which students could better understand and practice what they had learned (Wang, 2023;Zhang et al., 2023).”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Generate new curriculum content
What the sources say
- Partly supportedcites Wang, 2023 sourceExperimental — accuracy not yet measured
The Conclusions (page unavailable) say digital training should give translation students experience with practices that simulate professional life, and the Results describe practical skills training in a virtual community of practice. The paper does not establish the more specific claim that a smart classroom simulates a real classroom, practicum, or internship. (LLM, conf=0.84)
- Partly supportedcites Zhang et al., 2023 sourceExperimental — accuracy not yet measured
The Introduction describes smart classrooms using VR simulations to expose students to real-world situations, and the English Language and Literature Smart Classroom section describes simulations in which students apply what they have learned. Neither passage says the technology simulates a real classroom, practicum, or internship specifically. (LLM, conf=0.84)
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.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“These concerns negatively influenced the trust relationship between teachers and students.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Challenge existing assessment methods and strategies
What the sources say
- Partly supportedcites Farazouli et al. (2024) sourceExperimental — accuracy not yet measured
The ‘ChatGPT mediating role in assessment practices’ section says chatbot-related suspicion challenged teachers’ trust in students’ texts. It describes mistrust in the broader teacher–student relationship as a potential effect, rather than establishing that the relationship was negatively influenced. (LLM, conf=0.84)
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.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Much of the extant research on AI has focused on students (e.g., Chan and Hu, 2023;Crompton and Burke, 2023) or administrators (e.g., Nagy and Molontay, 2024;Teng et al., 2023).”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Introduction
What the sources say
- Not matchedcites Chan and Hu, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Supportedcites Crompton and Burke, 2023 source
The Crompton and Burke citation is attached to the student-focused part of the sentence, not the administrator-focused part. In “Intended user of AI,” the review reports that 72% of its 138 studies focused on students. (LLM, conf=0.94)
- Supportedcites Nagy and Molontay, 2024 source
The cited paper is an example of AI research involving higher-education decision-makers: its User experience research section reports a survey of 10 such participants and evaluates how they interpret AI explanations. The Introduction also describes the system as a decision-support tool intended to help decision-makers. (LLM, conf=0.88)
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.
- Not matched
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“A case study conducted in an Australian Master's program for Geographic Systems and Science found that ChatGPT, acting as a fictional student, effectively completed most assignments (e.g., coding; Stutz et al., 2023).”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Challenge existing assessment methods and strategies
What the sources say
- Partly supportedcites Stutz et al., 2023 sourceExperimental — accuracy not yet measured
The Introduction identifies the program as the UNIGIS MSc at the University of Salzburg, Austria—not an Australian program. The Approach describes ChatGPT's work submitted under a fictional student's name, and the lecturer's evaluation reports full marks on two assignments, 22/30 on a third, and an overall mark of 71%; the fourth assignment had substantial deficiencies. (LLM, conf=0.90)
plaindirectSingle case- 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.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Lopezosa et al. (2023) interviewed 32 journalism faculties from Spain and Latin America about how they perceived this new technology; however, no consensus on whether to integrate AI into the curriculum was identified.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Challenge existing curricula
What the sources say
- Partly supportedcites Lopezosa et al. (2023) sourceExperimental — accuracy not yet measured
The Discussion and conclusions (section 5; page unavailable) say there was no consensus among interviewed lecturers and researchers on how to integrate AI into communication curricula. But section 4.2 reports a high degree of consensus in favour of incorporating AI, so the claim overstates disagreement about whether to integrate it at all. The Methods (section 3; page unavailable) identify 32 interviewees, not 32 faculties. (LLM, conf=0.86)
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.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“They found that most participants considered ITS effective in providing customized instruction; however, the lack of support and guidance in using ITS brought the instructors substantial challenges.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Challenge existing instruction
What the sources say
- Supportedcites Al-Shanfari et al. (2023) source
The section on challenges when applying ITS identifies a lack of specialists to train instructors as the greatest challenge and insufficient technical support as another major challenge. The Conclusion also says participants viewed ITS as effective but considered training and support necessary for adoption. (LLM, conf=0.93)
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.
- Supported
None of the sources cited for this claim have been checked yet.
In the paper
“Ten studies described the ample new material AI provides for curriculum preparation and instruction implementation.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Generation of new material
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.
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
“Besides the above advantages, some challenges brought by AI in HE curricula, instruction, and assessment are described in six studies.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Challenges for CIA
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.
- Probably not a substantial claim. 55
- It may state two separate claims as one. 51
- The quote only partly states this claim. 3
At least one cited source doesn’t support this claim or points to a different paper, and none confirmed it — see the verdicts below.
In the paper
“In this light, understanding the benefits that AI brings to HE curriculum, instruction, and assessment could help academics make full use of the technology to reduce workloads (Holmes et al., 2023;Pereira et al., 2023) and improve productivity.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > The higher education curriculum-instruction-assessment (CIA) triad
What the sources say
- Disputedcites Holmes et al., 2023 sourceExperimental — accuracy not yet measured
The Teacher Perspective supports training academics to understand AI, but says there is currently little evidence that AI saves teachers’ time. The Conclusion calls claims that AI saves teachers’ time “questionable and unsubstantiated.” The paper therefore does not support the cited workload-reduction benefit; it does not establish improved productivity either. (LLM, conf=0.84)
Checked by a reasoning model — may be wrong.
- Not checked — no full text of the sourcecites Pereira et al., 2023 source
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. It may state two separate claims as one.
- Disputed
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“AI is found to accurately assess students' learning process and outcomes, and further determine teaching effect (Novais et al., 2023;Saad and Tounkara, 2023;Wang et al., 2025;Zhu et al., 2023).”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Assess students' learning process and outcomes
What the sources say
- Partly supportedcites Novais et al., 2023 sourceExperimental — accuracy not yet measured
The Discussion (page unavailable) says the fuzzy assessment system adequately described students’ soft-skills profiles using self- and peer-assessments, and the Feedback Reports and FSSA Validation section says it generated reports for 19 students. This supports a narrower claim about assessing soft skills in active-learning sessions. The paper does not establish accurate assessment of learning processes and outcomes generally or show that the system determined teaching effectiveness; its validation was limited to expert analysis. (LLM, conf=0.86)
- Not checked — no full text of the sourcecites Saad and Tounkara, 2023 source
- Not checked — no full text of the sourcecites Wang et al., 2025 source
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.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Applying AI technologies can facilitate analyzing students' learning procedures, performance, and needs, providing instructors with timely feedback, and assisting them in delivering adaptive instruction.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Personalized instruction
What the sources say
- Partly supportedcites Al-Shanfari et al., 2023 sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
- Partly supportedcites Firat, 2023 sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
- Not checked — no full text of the sourcecites Kohnke et al., 2023 source
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. The quote only partly states this claim.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Additionally, Zhang et al. (2023) designed and experimented with an intelligent classroom for English language and literature courses in China, and found that this AI tool provided the experimental group with a good learning environment and enhanced students' language proficiency.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Generate new curriculum content
What the sources say
- Partly supportedcites Zhang et al. (2023) sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
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.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Using experiments with small-samples, Zhu et al. (2023) developed in China an AI tool to predict students' performance based on their classroom behavior and previous performance.They suggested that this tool could be used to adjust instructors' teaching strategies and improve teaching quality.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Assess students' learning process and outcomes
What the sources say
- Supportedcites Zhu et al. (2023) source
Split into 2 atoms — 2 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
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.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“For instance, Wang (2023) stated that AI could make teaching content visualizable; that is, students could practice key communication competencies in a virtual community of practice, which improves teaching efficiency.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Generate new curriculum content
What the sources say
- Partly supportedcites Wang (2023) sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
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. It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“For instance, Archibald et al. (2023) showed that an AI-enabled discussion platform accurately calculated students' curiosity scores to present their engagement in discussion, further reducing teachers' assessment workload and facilitating their intervention based on the quality of posts written by students.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Assess students' learning process and outcomes
What the sources say
- Partly supportedcites Archibald et al. (2023) sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
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.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Both studies conducted focus group interviews, and revealed that AI, at curriculum levels, could provide instructors and students with new, rich, and personalized materials, contributing to curriculum design and development and facilitation of course preparation.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Generate new curriculum content
What the sources say
- Not checked — no full text of the sourcecites Chen et al., 2023 source
- Partly supportedcites Wang, 2023 sourceExperimental — accuracy not yet measured
The Literature Review says digital technology can customize learning modes, and the Results describe a course concept using digital educational technologies. But neither passage says AI provides new, rich, personalized materials or facilitates course preparation. The Methods describe an online conference and surveys, not focus-group interviews; the paper's development of a course concept does not establish that AI facilitated its design. (LLM, conf=0.79)
hedgeddirect- 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.
Every source the paper cites for this claim was checked and supports it.
In the paper
“AI could work as a virtual curriculum assistant that helps address students' time-consuming and repetitive questions about curriculum (e.g., content, time, deadline), reduce instructors' logistical workloads and give them more time to improve teaching quality and support students' development (Sajja et al., 2023).”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Work as a curriculum assistant
What the sources say
- Supportedcites Sajja et al., 2023 source
Split into 2 atoms — 2 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
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.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“For instance, Novais et al. ( 2023) designed an evaluation fuzzy expert system and employed it to build profiles of students' soft skills (e.g., communication and innovation skills, management skills, and social skills).”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Assess students' learning process and outcomes
What the sources say
- Supportedcites Novais et al. (2023) source
The Introduction describes the authors’ Fuzzy Soft Skills Assessment expert system as a tool that produces individual student profiles. The Conclusion reports that the system described students’ soft-skills profiles, and the Introduction identifies management, communication, innovation, and social skills as soft skills. (LLM, conf=0.91)
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.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“A new assessment method driven by AI tools (i.e., a backward propagation neural network) could automatically evaluate teaching, learning, and grading in an experiential online course in agriculture (Kumar et al., 2023).”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Assess students' learning process and outcomes
What the sources say
- Supportedcites Kumar et al., 2023 source
The Conclusions state that the study uses a backpropagation neural network to automate teaching, learning, and grading procedures for AI-assisted experiential agricultural education. The same section identifies the proposed model as an online practical agriculture course. (LLM, conf=0.94)
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.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“For example, Sajja et al. (2023) used the syllabus and other teaching materials to design a curriculum-oriented intelligent assistant and found that this virtualTA effectively provided accurate course information and improved students' course engagement.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Work as a curriculum assistant
What the sources say
- Partly supportedcites Sajja et al. (2023) sourceExperimental — accuracy not yet measured
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
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.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“PRISMA guidelines provide a structured framework for searching, identifying, and selecting articles, as well as extracting, analyzing, and synthesizing data to address specific research questions.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Search process
What the sources say
- Partly supportedcites Moher et al., 2009 sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
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.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“For instance, while AI can provide opportunities for instructors to personalize learning and provide students with more immediate feedback (Fauzi et al., 2023), it can raise concerns about academic integrity and the propagation of biased or inaccurate information.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Introduction
What the sources say
- Partly supportedcites Fauzi et al., 2023 sourceExperimental — accuracy not yet measured
The “Facilitate collaboration between students” section says students can use ChatGPT to share feedback and discuss subjects with lecturers. The “Providing useful information and resources” section says ChatGPT can answer students’ questions. Neither section says instructors can use AI to personalize learning or provide more immediate feedback; the feedback described is between students. (LLM, conf=0.79)
plainconditional- 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.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Firat (2023) also mentioned overreliance on AI, data privacy, and unequal access to AI tools as challenges.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Challenge existing instruction
What the sources say
- Partly supportedcites Firat (2023) sourceExperimental — accuracy not yet measured
The Abstract identifies privacy concerns as an issue requiring strategies, and the Discussion and conclusion calls for responsible and equitable AI implementation. But the passages do not identify overreliance on AI or unequal access to AI tools as instructional challenges; the call for equitable implementation alone does not establish the latter. (LLM, conf=0.86)
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.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“After removing duplications, 279 records were obtained for screening following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines (see Figure 1; Moher et al., 2009).”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Search process
What the sources say
- Supportedcites Moher et al., 2009 source
The Introduction identifies this paper as the statement of the PRISMA guidelines, and “From QUOROM to PRISMA” describes the flow diagram’s record-screening phase. The citation identifies the guidelines used; the 279 records are the citing authors’ own count, not a number attributed to Moher et al. (LLM, conf=0.94)
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.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Ethical concerns in instruction include incorrect or fabricated information, accessibility, and algorithm biases (Firat, 2023).”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Challenge existing instruction
What the sources say
- Partly supportedcites Firat, 2023 sourceExperimental — accuracy not yet measured
The Literature review and Discussion and conclusion (pages unavailable) identify misinformation generation and bias in ChatGPT as concerns in education. The paper calls for equitable implementation, but the passages do not identify accessibility as a specific ethical concern in instruction. (LLM, conf=0.85)
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.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“They found that most participants considered ITS effective in providing customized instruction; however, the lack of support and guidance in using ITS brought the instructors substantial challenges.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Challenge existing instruction
What the sources say
- Supportedcites Al-Shanfari et al. (2023) source
Section 4.4 says most instructors believed customized programming instruction helps students. The Discussion (Section 5) says instructors considered ITS effective because it supports one-on-one teaching, and the Conclusion (Section 6) reports that most participants recognized its effectiveness in supporting learning. No page numbers are available in our copy. (LLM, conf=0.90)
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.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Although most faculties embraced AI technology and suggested establishing AI as a standalone subject, some stated that challenges, limitations, and uncertainty about AI in education should be thoroughly researched before incorporating it into the curriculum.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Challenge existing curricula
What the sources say
- Supportedcites Lopezosa et al. (2023) source
Section 4.2 reports that some interviewees wanted the potential and implications of AI in university education thoroughly evaluated before its integration into communication faculties. Section 4.4 identifies limitations, challenges, and uncertainty as reasons some interviewees urged caution about adding a dedicated AI course. (LLM, conf=0.89)
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.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Enthusiasm of technologists for using machines to replace the labor of humans (Brown, 2020) is clearly a threat to the human-in-the-loop.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Threats AI brings to higher education
What the sources say
- Partly supportedcites Brown, 2020 sourceExperimental — accuracy not yet measured
In “Scenario 5: The Likely Schooling Future?” (page unavailable), Brown describes arguments for ICT-based learning instead of human teachers and says replacing human-to-human schooling seems contraindicated. In “Schooling in COVID-19” (page unavailable), he mentions people enthusiastic about e-learning, but does not identify technologists as enthusiastic about replacing human labor generally. The citation supports the narrower concern about replacing teachers with technology, not the broader characterization. (LLM, conf=0.82)
boosteddirect- 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.
- Rests on an assumption: AI is simply programming and thus not human.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“In terms of existing curricula, there is considerable debate as to what students need to be taught about or with AI and how it could be integrated (Lopezosa et al., 2023).”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Threats AI brings to higher education
What the sources say
- Supportedcites Lopezosa et al., 2023 source
Section 4.5 describes differing areas of AI knowledge and uses to teach journalism students. Section 5 reports no consensus among interviewed lecturers and researchers on how to integrate AI into communication curricula, identifying three positions. The paper supports the claim in the context of journalism and communication education. (LLM, conf=0.88)
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.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Indeed, an important threat AI brings to education is the requirement that all teaching and learning has to happen in an ICT environment, which could be seen as antithetical to the human in the human experience of learning (Brown, 2020).”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Threats AI brings to higher education
What the sources say
- Partly supportedcites Brown, 2020 sourceExperimental — accuracy not yet measured
In “Scenario 5: The Likely Schooling Future?” (page unavailable), Brown warns that reducing schooling to computer interaction limits opportunities to learn human skills and argues against replacing human-to-human schooling. But Brown describes an ICT-only future as a possibility, not a requirement imposed by AI; the paper mentions AI separately as a technology that cannot yet mimic humanity. (LLM, conf=0.88)
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.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Although most faculties embraced AI technology and suggested establishing AI as a standalone subject, some stated that challenges, limitations, and uncertainty about AI in education should be thoroughly researched before incorporating it into the curriculum.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Challenge existing curricula
What the sources say
- Partly supportedcites Lopezosa et al. (2023) sourceExperimental — accuracy not yet measured
Section 4.2 reports strong agreement among interviewed lecturers and researchers about incorporating AI into communication education. Section 5 calls a standalone AI subject the most widely accepted position, but section 4.4 describes three competing views; the paper does not establish that a majority of interviewees favored a standalone subject or report a position held by faculties as institutions. Page numbers were not available in our copy. (LLM, conf=0.85)
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.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“They found that teachers tended to be critical about students' texts, underestimated students' performance, and doubted that some student texts had been finished by GPT.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Challenge existing assessment methods and strategies
What the sources say
- Supportedcites Farazouli et al. (2024) source
In ‘Participants’ assessment of student responses,’ the paper says teachers were more critical of student-written texts and often downgraded responses previously graded A or C. The Discussion likewise reports downgrading and says teachers perceived some student texts as chatbot-written, questioning student authorship. (LLM, conf=0.90)
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.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The fear and possibility of non-detectable academic dishonesty will require substantial efforts to ensure the integrity and social warrant (Brown, 2022) of course grades and academic qualifications.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Threats AI brings to higher education
What the sources say
- Partly supportedcites Brown, 2022 sourceExperimental — accuracy not yet measured
In “The past,” Brown describes invigilated examinations designed to ensure integrity and qualifications that carried public recognition, supporting the general concern about the social credibility of assessment. The cited paper does not address non-detectable dishonesty or say that it will require substantial future efforts to protect course grades and qualifications. (LLM, conf=0.80)
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.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Some individuals suggested a compromise idea of integrating AI into communication subjects as a preliminary step (Lopezosa et al., 2023).”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Challenge existing curricula
What the sources say
- Supportedcites Lopezosa et al., 2023 source
Section 4.4 reports that some interviewees proposed integrating AI content into existing journalism or advertising classes while the technology is still developing, with dedicated AI subjects possible later. This supports the proposed preliminary step. (LLM, conf=0.93)
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: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Firat (2023) indicated that implementing AI may require educators to change their role from being instructors to guides or facilitators.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Challenge existing instruction
What the sources say
- Supportedcites Firat (2023) source
The Discussion and conclusion says AI can relieve university staff of routine tasks so they can focus on higher-order skills and mentoring. That supports the hedged description of educators moving toward a guiding role, even though the paper does not use the words “instructors” and “facilitators.” (LLM, conf=0.84)
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.
- Supported
None of the sources cited for this claim have been checked yet.
In the paper
“The operability of this system was further supported by assessing the teaching quality and effect of two classes, and the AI results showed that both classes' teaching quality scored almost 7 out of 10, suggesting a need to improve.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Assess teaching effect
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.
In the paper
“Similarly, Tang et al. (2023) discussed how a designed intelligent evaluation system could better recognize voices, face, postures, and teaching skills in microteaching skill training, accurately assess preservice teachers' teaching performance, and provide accurate guidance.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Assess students' learning process and outcomes
What the sources say
- Not checked — no full text of the sourcecites Tang et al. (2023) source
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. It may state two separate claims as one.
In the paper
“Pereira et al. (2023) described how an emerging recommender system generated equivalent questions for assignments and exams, to enhance the variation of assignments and support instructors in preparing individualized assignments and minimizing plagiarism.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Prepare personalized assignments
What the sources say
- Not checked — no full text of the sourcecites Pereira et al. (2023) source
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.
None of the sources cited for this claim have been checked yet.
In the paper
“2023) study, conducted in the Chinese context, showed that a newly designed speech recognition method, based on a recurrent neural network algorithm, had a better accuracy rate and faster convergence, and could replace the previous method and effectively address issues of the low speech recognition rate caused by noisy environments.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Generate new curriculum content
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.
None of the sources cited for this claim have been checked yet.
In the paper
“They found that this model correctly assigned 85% of students to the correct clusters (i.e., at risk or leader), and assisted instructors in making correct decisions.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Assess students' learning process and outcomes
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.
None of the sources cited for this claim have been checked yet.
In the paper
“AI-generated scores were compared with real scores, providing reliable feedback to instructors and students.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Assess students' learning process and outcomes
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
“By implementing embedded glasses in real classrooms, Li L. et al. (2023) showed that this device helped instructors recognize and process students' real-time images and emotions and keep abreast of their learning status, and this information further provided timely feedback to instructors to change their teaching strategies.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Personalized instruction
What the sources say
- Not matchedcites Li L. et al. (2023)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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.
- Not matched
In the paper
“Similarly, a new piano teaching mode powered by a vocal music singing learning system has been demonstrated to be relatively successful: it not only made piano teaching more personalized and intelligent, increased teaching efficacy by 7.31% compared to the traditional teaching mode, but also motivated students to engage more in piano practice time and classroom participation (Li Q. et al., 2023).”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Personalized instruction
What the sources say
- Not matchedcites Li Q. et al., 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boosteddirect- 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.
- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Asia, predominantly China, accounted for 17 of the 33 studies.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Nature of studies
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
“Wang et al. (2025) combined human-computer interaction and deep learning algorithm to design an intelligent evaluation system for innovation and entrepreneurship.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Assess teaching effect
What the sources say
- Not checked — no full text of the sourcecites Wang et al. (2025) source
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 result the paper reports from its own data — 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 simulation results also suggested that AI-powered teaching mode had the potential to be implemented in real classrooms.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Generate new curriculum content
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.
None of the sources cited for this claim have been checked yet.
In the paper
“The system could detect students' attitudes and behaviors and assess teachers' teaching preparation, language expression, content mastery, and teaching design.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Assess teaching effect
hedgeddirect- 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 result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Regarding research methods, 15 of the studies used modeling or simulation methods to design, implement, and test the accuracy and effect of AI tools.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Nature of studies
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.
The quote only partly states this claim.
None of the sources cited for this claim have been checked yet.
In the paper
“A new assessment method driven by AI tools could help instructors prepare personalized assignments.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Prepare personalized assignments
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.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“Therefore, compared to the control group, the teaching effect of the experiment group increased by 9.44%, and students reported more satisfaction with teaching.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Personalized instruction
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 result the paper reports from its own data — 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 records were excluded because their titles and abstracts showed that (a) they did not investigate how AI affected HE curriculum, instruction, and assessment (n =135), (b) they lacked empirical evidence (n = 63), or (c) they did not focus on university contexts (n = 8).”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Search process
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 claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Additionally, AI seems to benefit assessments by generating personalized assignments (Pereira et al., 2023), effectively assessing and predicting students' academic achievement (Wang et al., 2025) and non-cognitive outcomes (e.g., soft skills, Novais et al., 2023), identifying disadvantaged students (Saad and Tounkara, 2023), and assessing teaching effectiveness (Wang et al., 2025).”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Benefits of AI in higher education
What the sources say
hedgeddirect- 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
“The remaining 73 records were downloaded for full-text screening.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Search process
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.
None of the sources cited for this claim have been checked yet.
In the paper
“Moreover, it is hard for humans or AI detectors to identify AIgenerated texts or assignments, which in turn challenges existing assessment practices and strategies.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Challenge existing assessment methods and strategies
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 result the paper reports from its own data — 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 titles and abstracts of these records were assessed using the agreed inclusion and exclusion criteria (see Table 1), resulting in the exclusion of 206 records.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Search process
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.
None of the sources cited for this claim have been checked yet.
In the paper
“AI is found to bring many challenges to curriculum developers and existing curricula, especially in deciding what content is more valuable, how to integrate AI into the current curriculum, and how to prepare students with digital literacy.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Challenge existing curricula
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 result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Of the 33 studies, taking into account all overlapping categories, 21 (64%) papers had something to do with instruction, about half had something to do with assessment (17, 52%), and about a quarter focused on curriculum (9, 27%).”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Education dimension
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
“Similarly, Kohnke et al. (2023) found that AI challenged the current assessment system, as instructors were worried that AI tools are too convenient for students making it easy to cheat and not work independently.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Challenge existing assessment methods and strategies
What the sources say
- Not checked — no full text of the sourcecites Kohnke et al. (2023) source
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 result the paper reports from its own data — 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 Table 2 shows, the balance was distributed widely across the world.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Nature of studies
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 result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Specifically, studies that introduced AI or HE curriculum, instruction, and assessment but did not actually explore the relationship between them were excluded (n = 32).”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Search process
plainn = 32- 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 result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Three distinct foci of AI were examined.The most common focus in 16 studies was the technological dimensions of AI, such as designing and modeling an AI tool for HE curriculum, instruction, and assessment and testing the accuracy of this tool itself.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Nature of studies
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. The quote only partly states this claim.
A result the paper reports from its own data — 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 shown in Figure 2, 22 of the studies addressed just one of the three aspects, with most being in instruction and assessment.Just 11 studies attempted an integration between two or more of the three aspects.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Education dimension
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
“Thus, AI has been found to bring benefits to HE curriculum, instruction, and assessment, including generating new materials, alleviating faculty workloads, and automating or optimizing assessment, in alignment with progressive literature”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Benefits of AI in higher 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: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Chan (2023) stated that new concerns in HE assessment have emerged, as most students and teachers are worried that some students use AI tools to cheat and plagiarize, and teachers could not identify such dishonesty correctly.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Challenge existing assessment methods and strategies
What the sources say
- Not matchedcites Chan (2023)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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.
Probably not a substantial claim. It may state two separate claims as one.
- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“This review first reveals that AI can create new courses and resources, promote curriculum development, address time-consuming workloads concerning curriculum (e.g., questions about syllabi, time, and deadline), and evaluate the material difficulty and quality”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Benefits of AI in higher 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: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Chan (2023) indicated that AI may cause overdependence on technology and weaken social connections between teachers and students.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Challenge existing instruction
What the sources say
- Not matchedcites Chan (2023)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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.
- Not matched
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
“Considerable enthusiasm exists for the integration of AI skills with other graduate attributes such as the 4C skills (i.e., communication, collaboration, critical thinking, and creativity).”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Threats AI brings to higher 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: 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
“The literature reported here makes clear substantial challenges to curriculum, instruction, and assessment.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Threats AI brings to higher education
boosteddirect- 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 result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Other articles were removed because they (a) did not have empirical evidence (n = 4), (b) were in a non-HE context (n = 1), (c) were not available as full text (n = 2), and (d) were not in English (n = 1).”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Search process
plainn = 4- 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.
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
“Related to this is the lack of crossdisciplinary collaborative research between computer scientists and social scientists.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Weaknesses in the research
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.
None of the sources cited for this claim have been checked yet.
In the paper
“Meanwhile, AI, as virtual teachers, could take up logistical workloads (e.g., reinforce students' mastery of key concepts) and provide instructors time and energy to conduct personalized instruction and satisfy students' distinct needs”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Benefits of AI in higher education
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.
In the paper
“Chen et al. (2023) interviewed 16 faculty members in data science and revealed that inconsistent definitions of data science, inadequate team support, and lack of collaboration platforms were major challenges.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Challenge existing instruction
What the sources say
- Not checked — no full text of the sourcecites Chen et al. (2023) source
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
“There are some concerns about using AI in HE instruction, including challenging teacher's AI teaching competencies, ethical considerations, and lack of teaching support.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Challenge existing instruction
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 result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There are 16 countries around the world contributing to this field.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Nature of studies
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 result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Twenty-five initial descriptive codes (e.g., improve teaching effectiveness, challenge the role of educators, assess teaching effect) were captured.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Data extraction and analysis
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
“Articles could be arranged into more than one theme because of the presence of multiple themes.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Data extraction and analysis
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: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — 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 total of 2,810 articles were identified.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Method
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.
None of the sources cited for this claim have been checked yet.
In the paper
“The most frequently mentioned challenge is that AI has been proven to pass many examinations and assignments.Consequently, some students may use it to cheat or plagiarize.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Challenge existing assessment methods and strategies
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.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To establish trustworthiness, the research team made agreements on search terms and initial inclusion and exclusion criteria before the first author identified the literature.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Method
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
“Not only would this tool ensure the alignment of assessment reading materials with course reading materials, but also provide valid evidence for the assessment materials.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Work as a curriculum assistant
hedgeddirect- 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 authors state this as an assumption: Reading in an assessment should not be harder than that used in instruction.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Filtering only to include peer-reviewed journal articles helped ensure the quality of literature in the search phases.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Method
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.
In the paper
“Thus, curriculum, instruction, and assessment comprise the essential triad of all educational practices (Pellegrino, 2006).”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > The higher education curriculum-instruction-assessment (CIA) triad
What the sources say
- Not checked — no full text of the sourcecites Pellegrino, 2006
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.
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
“The threat of AI applies also to instructors' role and their teaching abilities.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Threats AI brings to higher education
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.
- Rests on an assumption: Most academics have little understanding of how AI tools are designed and what large language models can do.
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 opportunities point to the development of interdisciplinary courses with the help of AI, especially in terms of course content and assessment design.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Opportunities of AI in higher 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: 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 presence of AI seems to create opportunities for academics in terms of revisions to existing courses and freeing up time to focus on improving existing curriculum, instruction, and assessment quality.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Opportunities of 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. 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, few have thought constructively about how to integrate AI into their teaching.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Threats AI brings to higher 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.
- Rests on an assumption: Most academics have little understanding of how AI tools are designed and what large language models can do.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“During the screening stage, either author was unsure if a specific article should be included, and then the content of this article was discussed against the research question and focus of this review.These discussions resulted in refining the inclusion and exclusion criteria and a consensus on included articles.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Search process
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
“Application within humanities may be much more feasible with the current capacities of GenAI, but still academics have to learn how AI can be an adjunct to teaching rather than potentially a substitute for the instructor's knowledge and skill.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Threats AI brings to higher 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. It may state two separate claims as one.
In the paper
“The other authors critically read and reviewed the coding results, final synthesis, and interpretation of the themes.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Data extraction and analysis
What the sources say
- Not matchedcites Patton, 2003
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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.
- Not matched
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 early research, however, is potentially problematic because of its narrowness.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Weaknesses in the research
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
“AI creates the possibility that skill with large language models (e.g., to analyze data, to compose communication) is what students might need in the future.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Threats AI brings to higher 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: 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 most important challenge centers around assessment and evaluation of learning.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Threats AI brings to higher 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.
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 clear generative AIs will force academics to rethink the purpose of assessment (e.g., studentcentered or knowledge-based learning), the content and format of what is assessed, the design of assessments (e.g., process evaluation, outcome evaluation, or value-added evaluation), and the formative use of assessed performances.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Threats AI brings to higher education
boosteddirect- 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
“It is also likely that the challenges associated with meaningful integration of AI into curriculum, instruction, and assessment will remain difficult for years to come.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Conclusion
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: 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
“While the future remains uncertain, several of the trends found in the study are likely to continue for some time to come.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Conclusion
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.
The paper relies on this without support.
The first nine months of literature could capture the critical early phase when educators and researchers started to publish responses to newly released AI tools such as ChatGPT.
“The first 9 months of literature could capture the critical early phase, when educators and researchers started to publish their responses to newly released AI tools, such as ChatGPT.”
The authors state this as an assumption.
Reading in an assessment should not be harder than that used in instruction.
“on the assumption that reading in an assessment should not be harder than that used in instruction.”
The paper relies on this without support.
Most academics have little understanding of how AI tools are designed and what large language models can do.
“Most academics have little understanding of how AI tools are designed and what large language models can do.”
The paper relies on this without support.
Currently AI cannot identify fabrication or error in the text that it assembles.
“This is all the more important because currently AI cannot identify fabrication or error in the text that it assembles.”
The paper relies on this without support.
AI is simply programming and thus not human.
“While AI seems to be able to do many things, it is simply programming and thus not human.”
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
“Liang et al. (2025) evaluaron el impacto temprano de la IA en prácticas de enseñanza, proponiendo líneas futuras de investigación orientadas a la co-diseño con comunidades educativas [10].”
From Avances, Retos Éticos y Perspectivas de la Inteligencia Artificial (IA) en la Educación de América Latina (2025) · cites this paper as [10]
Experimental — accuracy not yet measured
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
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Generation of new material
“Ten studies described the ample new material AI provides for curriculum preparation and instruction implementation.Attributes mentioned include providing various resources and generating new teaching content, building an immersive learning environment, and improving or replacing existing teaching modes with a new teaching approach”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Generate new curriculum content
“Additionally, Zhang et al. (2023) designed and experimented with an intelligent classroom for English language and literature courses in China, and found that this AI tool provided the experimental group with a good learning environment and enhanced students' language proficiency.”
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
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Nature of studies
“Asia, predominantly China, accounted for 17 of the 33 studies.”
§ A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment > Weaknesses in the research
“The currently available research has been conducted largely in Western, Educated, Industrialized, Rich, and Democratic (WEIRD;Henrich et al., 2010) societies.”
Cited works that were retracted
Works this paper cites that were later retracted, from the retraction notices publishers deposit. Major when the notice came out before this paper was published.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
“Teng, 2023, Data-driven decision-making model based on artificial intelligence in higher education system of colleges and universities, Expert Syst., 40, e12820, 10.1111/exsy.12820”
Cited work 10.1111/exsy.12820 · retracted 2024-12-09 · notice 10.1111/exsy.13810 · this paper published 2025-04-25
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
“Guo, 2023, Innovative application of sensor combined with speech recognition technology in college english education in the context of artificial intelligence, J. Sens., 2023, 9281914, 10.1155/2023/9281914”
Cited work 10.1155/2023/9281914 · retracted 2023-12-20 · notice 10.1155/2023/9870608 · this paper published 2025-04-25
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
“Li, 2023, Artificial intelligence facial recognition and voice anomaly detection in the application of English MOOC teaching system, Soft Comput., 27, 6855, 10.1007/s00500-023-08119-7”
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Metadata
- Authors
- Liang, J., Stephens, J. M., & Brown, G. T. L.
- Affiliations
- University of Auckland (NZ)
- Corresponding author
- Liang, Jingjing
- Venue
- Frontiers in Education
- Publisher
- Frontiers Media SA
- Published
- 2025-04-25
- Volume
- 10
- ISSN
- 2504-284X (ISSN-L)
- OpenAlex
- W4409804936
- Semantic Scholar
- a7c51421c732861e393840cdeeeda3e0291ae23e
- Cited by
- 112
- References
- 53
- Citations by year
- 2026: 88 · 2025: 24
- FWCI
- 13.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Education (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- ResearchSpace (University of Auckland) (submittedVersion, cc-by)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Education › Educational Technology and Pedagogy
- Keywords
- generative AI, ChatGPT, higher education, course program, instruction, assessment, faculty perspectives, staff workload, automated assessment, AI-generated content, artificial intelligence in education
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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.
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