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Cited by 112Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationEducational Technology and Pedagogy

A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment

Open access
Liang, Jingjing, Stephens, Jason M., Brown, Gavin T. L.·2025·Frontiers in Education
DOI 10.3389/feduc.2025.1522841
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Abstract

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.

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.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3389/feduc.2025.1522841

Cites

53 of 53 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

17 of 69 cited sources checked · 8 supported · 2 flagged · accuracy not yet measured

  • Findingcitation

    Ten studies demonstrated that AI could support staff in curriculum, instruction, and assessment by reducing their logistical workloads, especially in curriculum design, interactions with students, delivering personalized instruction, and preparing adapted or personalized assignments.

    “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”
    Quote in the paperSubstantial claim1 of 3 sources checked
    • cites Holmes et al., 2023 source

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  • Findingcitation

    According to Pisica et al. (2023), 18 academics from Romanian universities reported benefits of AI in curriculum, including generating new content for existing courses and developing new curricula or disciplines.

    “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.”
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    • cites Pisica et al. (2023) source
  • Findingcitation

    AI technology such as smart classroom enables simulation of the atmosphere of a real classroom, practicum, or internship, in which students could better understand and practice what they had learned.

    “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).”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites Wang, 2023 source

      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)

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    • cites Zhang et al., 2023 source

      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)

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  • Premisecitation

    Much extant research on AI has focused on students or administrators.

    “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).”
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    • cites Chan and Hu, 2023

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    • cites Crompton and Burke, 2023 source
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    Ten studies described that AI provides ample new material for curriculum preparation and instruction implementation.

    “Ten studies described the ample new material AI provides for curriculum preparation and instruction implementation.”
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    Premisecitation

    Understanding the benefits that AI brings to HE curriculum, instruction, and assessment could help academics make full use of the technology to reduce workloads and improve productivity.

    “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.”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites Holmes et al., 2023 source

      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)

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    • cites Pereira et al., 2023 source

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    AI is found to accurately assess students' learning process and outcomes, and further determine teaching effect.

    “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).”
    Quote in the paperSubstantial claim1 of 3 sources checked
    • cites Novais et al., 2023 source

      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)

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    • cites Saad and Tounkara, 2023 source
    • cites Wang et al., 2025 source

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    Findingcitation

    Applying AI technologies can facilitate analyzing students' learning procedures, performance, and needs, providing instructors with timely feedback, and assisting them in delivering adaptive instruction; consequently, teaching and learning effects were somewhat improved.

    “Applying AI technologies can facilitate analyzing students' learning procedures, performance, and needs, providing instructors with timely feedback, and assisting them in delivering adaptive instruction.”
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    • cites Al-Shanfari et al., 2023 source

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    • cites Firat, 2023 source

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    • cites Kohnke et al., 2023 source

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    Findingcitation

    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).

    “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).”
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    • cites Novais et al. (2023) source

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    Findingcitation

    Overreliance on AI, data privacy, and unequal access to AI tools are challenges in instruction.

    “Firat (2023) also mentioned overreliance on AI, data privacy, and unequal access to AI tools as challenges.”
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    • cites Firat (2023) source

      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)

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    Findingcitation

    After removing duplications, 279 records were obtained for screening following PRISMA guidelines.

    “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).”
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    Findingcitation

    Most instructors in Omani universities considered intelligent tutoring systems effective in providing customized instruction.

    “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.”
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    Findingcitation

    Most interviewed journalism faculties embraced AI technology and suggested establishing AI as a standalone subject.

    “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.”
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    • cites Lopezosa et al. (2023) source

      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)

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    Findingcitation

    Teachers in humanities and social sciences tended to be critical about students' texts, underestimated students' performance, and doubted that some student texts had been finished by GPT.

    “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.”
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    • cites Farazouli et al. (2024) source

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    Findingcitation

    Some interviewed journalism faculties suggested integrating AI into communication subjects as a preliminary step.

    “Some individuals suggested a compromise idea of integrating AI into communication subjects as a preliminary step (Lopezosa et al., 2023).”
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    • cites Lopezosa et al., 2023 source

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    Findingcitation

    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.

    “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.”
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    Findingcitation

    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.

    “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.”
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    Findingcitation

    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.

    “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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Pereira et al. (2023) source

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    Findingcitation

    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.

    “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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Zhang et al. (2023) source

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    Findingcitation

    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.

    “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.”
    Quote in the paperNot a substantial claim

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    Findingcitation

    The model correctly assigned 85% of students to the correct clusters (i.e., at risk or leader), and assisted instructors in making correct decisions.

    “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.”
    Quote in the paperNot a substantial claim

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    Findingcitation

    AI-generated scores were compared with real scores, providing reliable feedback to instructors and students.

    “AI-generated scores were compared with real scores, providing reliable feedback to instructors and students.”
    Quote in the paperNot a substantial claim

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    Findingcitation

    Li L. et al. (2023) showed that embedded glasses in real classrooms 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.

    “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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Li L. et al. (2023)

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

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    Findingcitation

    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.

    “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.”
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    Findingcitation

    A new piano teaching mode powered by a vocal music singing learning system made piano teaching more personalized and intelligent, increased teaching efficacy by 7.31% compared to the traditional teaching mode, and motivated students to engage more in piano practice time and classroom participation.

    “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).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Li Q. et al., 2023

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

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    Findingcitation

    Wang (2023) stated that AI could make teaching content visualizable; students could practice key communication competencies in a virtual community of practice, which improves teaching efficiency.

    “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.”
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    Findingown data

    Of the 33 included studies, 17 were conducted in Asia, predominantly China.

    “Asia, predominantly China, accounted for 17 of the 33 studies.”
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    Findingcitation

    Wang et al. (2025) combined human-computer interaction and deep learning algorithm to design an intelligent evaluation system for innovation and entrepreneurship.

    “Wang et al. (2025) combined human-computer interaction and deep learning algorithm to design an intelligent evaluation system for innovation and entrepreneurship.”
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    • cites Wang et al. (2025) source

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    Findingcitation

    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.

    “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.”
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    Findingcitation

    AI can provide instructors and students with new, rich, and personalized materials, contributing to curriculum design and development and facilitation of course preparation.

    “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.”
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    • cites Chen et al., 2023 source
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    Simulation results suggested that AI-powered teaching mode had the potential to be implemented in real classrooms.

    “The simulation results also suggested that AI-powered teaching mode had the potential to be implemented in real classrooms.”
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    The system could detect students' attitudes and behaviors and assess teachers' teaching preparation, language expression, content mastery, and teaching design.

    “The system could detect students' attitudes and behaviors and assess teachers' teaching preparation, language expression, content mastery, and teaching design.”
    Quote in the paperSubstantial claim

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    Findingown data

    15 of the 33 included studies used modeling or simulation methods.

    “Regarding research methods, 15 of the studies used modeling or simulation methods to design, implement, and test the accuracy and effect of AI tools.”
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    Findingcitation

    AI could work as a virtual curriculum assistant that helps address students' time-consuming and repetitive questions about curriculum, reduce instructors' logistical workloads and give them more time to improve teaching quality and support students' development.

    “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).”
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    • cites Sajja et al., 2023 source

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    A new assessment method driven by AI tools could help instructors prepare personalized assignments.

    “A new assessment method driven by AI tools could help instructors prepare personalized assignments.”
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    Findingcitation

    Compared to the control group, the teaching effect of the experiment group increased by 9.44%, and students reported more satisfaction with teaching.

    “Therefore, compared to the control group, the teaching effect of the experiment group increased by 9.44%, and students reported more satisfaction with teaching.”
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    Findingcitation

    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.

    “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).”
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    • cites Kumar et al., 2023 source

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    Findingcitation

    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.

    “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.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Sajja et al. (2023) source

      Checked before — its earlier verdict is held back until a new check runs.

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    Findingown data

    Of the 206 excluded records, 135 did not investigate how AI affected HE curriculum, instruction, and assessment, 63 lacked empirical evidence, and 8 did not focus on university contexts.

    “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).”
    Quote in the paperNot a substantial claim

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    Findingcitation

    AI benefits assessments by generating personalized assignments, effectively assessing and predicting students' academic achievement and non-cognitive outcomes, identifying disadvantaged students, and assessing teaching effectiveness.

    “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).”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites Pereira et al., 2023 source
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    • cites Saad and Tounkara, 2023 source

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    Findingnone given

    The remaining 73 records were downloaded for full-text screening.

    “The remaining 73 records were downloaded for full-text screening.”
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    Findingcitation

    It is hard for humans or AI detectors to identify AI-generated texts or assignments, which challenges existing assessment practices and strategies.

    “Moreover, it is hard for humans or AI detectors to identify AIgenerated texts or assignments, which in turn challenges existing assessment practices and strategies.”
    Quote in the paperSubstantial claim

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    Findingown data

    Title and abstract screening excluded 206 records.

    “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.”
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    Findingcitation

    Ethical concerns in instruction include incorrect or fabricated information, accessibility, and algorithm biases.

    “Ethical concerns in instruction include incorrect or fabricated information, accessibility, and algorithm biases (Firat, 2023).”
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    • cites Firat, 2023 source

      Not checked in the free run.

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    Findingcitation

    AI brings 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.

    “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.”
    Quote in the paperSubstantial claim

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    Findingown data

    Of the 33 studies, 21 (64%) had something to do with instruction, 17 (52%) with assessment, and 9 (27%) with curriculum.

    “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%).”
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    Findingcitation

    Some interviewed journalism faculties stated that challenges, limitations, and uncertainty about AI in education should be thoroughly researched before incorporating it into the curriculum.

    “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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Lopezosa et al. (2023) source

      Checked before — its earlier verdict is held back until a new check runs.

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    Findingcitation

    Instructors were worried that AI tools are too convenient for students, making it easy to cheat and not work independently.

    “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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Kohnke et al. (2023) source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The balance was distributed widely across the world.

    “As Table 2 shows, the balance was distributed widely across the world.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Full-text screening excluded 32 studies that introduced AI or HE curriculum, instruction, and assessment but did not actually explore the relationship between them.

    “Specifically, studies that introduced AI or HE curriculum, instruction, and assessment but did not actually explore the relationship between them were excluded (n = 32).”
    Quote in the paperNot a substantial claim

  • May combine two claims. The quote only partly states this claim.

    Findingown data

    Three distinct foci of AI were examined: technology (16 studies), human experience (10 studies), and use of AI in class (7 studies).

    “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.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    22 of the studies addressed just one of the three aspects, with most being in instruction and assessment; 11 studies attempted an integration between two or more of the three aspects.

    “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.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingcitation

    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.

    “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”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Findingcitation

    Most students and teachers are worried that some students use AI tools to cheat and plagiarize, and teachers could not identify such dishonesty correctly.

    “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.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Chan (2023)

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingcitation

    There is considerable debate as to what students need to be taught about or with AI and how it could be integrated.

    “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).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Lopezosa et al., 2023 source

      Checked before — its earlier verdict is held back until a new check runs.

  • May combine two claims.

    Findingcitation

    AI can create new courses and resources, promote curriculum development, address time-consuming workloads concerning curriculum, and evaluate the material difficulty and quality.

    “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”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingcitation

    AI may cause overdependence on technology and weaken social connections between teachers and students.

    “Chan (2023) indicated that AI may cause overdependence on technology and weaken social connections between teachers and students.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Chan (2023)

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingnone given

    Considerable enthusiasm exists for the integration of AI skills with other graduate attributes such as the 4C skills.

    “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).”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Findingreasoning

    The literature reported here makes clear substantial challenges to curriculum, instruction, and assessment.

    “The literature reported here makes clear substantial challenges to curriculum, instruction, and assessment.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    Other articles were removed because they did not have empirical evidence (n=4), were in a non-HE context (n=1), were not available as full text (n=2), and were not in English (n=1).

    “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).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingreasoning

    There is a lack of crossdisciplinary collaborative research between computer scientists and social scientists.

    “Related to this is the lack of crossdisciplinary collaborative research between computer scientists and social scientists.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingcitation

    AI as virtual teachers could take up logistical workloads and provide instructors time and energy to conduct personalized instruction and satisfy students' distinct needs.

    “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”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingcitation

    Inconsistent definitions of data science, inadequate team support, and lack of collaboration platforms were major challenges for faculty members in data science.

    “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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Chen et al. (2023) source

  • May combine two claims.

    Findingcitation

    Concerns about using AI in HE instruction include challenging teachers' AI teaching competencies, ethical considerations, and lack of teaching support.

    “There are some concerns about using AI in HE instruction, including challenging teacher's AI teaching competencies, ethical considerations, and lack of teaching support.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    There are 16 countries around the world contributing to this field.

    “There are 16 countries around the world contributing to this field.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Twenty-five initial descriptive codes were captured.

    “Twenty-five initial descriptive codes (e.g., improve teaching effectiveness, challenge the role of educators, assess teaching effect) were captured.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingreasoning

    Articles could be arranged into more than one theme because of the presence of multiple themes.

    “Articles could be arranged into more than one theme because of the presence of multiple themes.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    A total of 2,810 articles were identified.

    “A total of 2,810 articles were identified.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingcitation

    Implementing AI may require educators to change their role from instructors to guides or facilitators.

    “Firat (2023) indicated that implementing AI may require educators to change their role from being instructors to guides or facilitators.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Firat (2023) source

      Not checked in the free run.

  • May combine two claims.

    Findingcitation

    The most frequently mentioned challenge in HE assessment is that AI has been proven to pass many examinations and assignments, so some students may use it to cheat or plagiarize.

    “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.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    To establish trustworthiness, the research team made agreements on search terms and initial inclusion and exclusion criteria before the first author identified the literature.

    “To establish trustworthiness, the research team made agreements on search terms and initial inclusion and exclusion criteria before the first author identified the literature.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    The AI tool would ensure the alignment of assessment reading materials with course reading materials and provide valid evidence for the assessment materials.

    “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.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Reading in an assessment should not be harder than that used in instruction.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Study design and conductnone given

    Filtering only to include peer-reviewed journal articles helped ensure the quality of literature in the search phases.

    “Filtering only to include peer-reviewed journal articles helped ensure the quality of literature in the search phases.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    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; they help ensure the quality of the review, minimize bias, and maintain transparency and replicability.

    “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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Moher et al., 2009 source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation

    Curriculum, instruction, and assessment comprise the essential triad of all educational practices.

    “Thus, curriculum, instruction, and assessment comprise the essential triad of all educational practices (Pellegrino, 2006).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Pellegrino, 2006

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    AI can provide opportunities for instructors to personalize learning and provide students with more immediate feedback.

    “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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Fauzi et al., 2023 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    The threat of AI applies also to instructors' role and their teaching abilities.

    “The threat of AI applies also to instructors' role and their teaching abilities.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Most academics have little understanding of how AI tools are designed and what large language models can do.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationreasoning

    These opportunities point to the development of interdisciplinary courses with the help of AI, especially in terms of course content and assessment design.

    “These opportunities point to the development of interdisciplinary courses with the help of AI, especially in terms of course content and assessment design.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationnone given

    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.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationreasoning

    Few academics have thought constructively about how to integrate AI into their teaching.

    “Thus, few have thought constructively about how to integrate AI into their teaching.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Most academics have little understanding of how AI tools are designed and what large language models can do.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    During the screening stage, when either author was unsure if a specific article should be included, 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.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

    Interpretationreasoning

    Application within humanities may be much more feasible with the current capacities of GenAI, but academics still have to learn how AI can be an adjunct to teaching rather than a substitute for the instructor's knowledge and skill.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductcitation

    During data extraction and analysis, the first author coded key information from each study; the other authors critically read and reviewed the coding results, final synthesis, and interpretation of the themes; uncertainty on internal homogeneity and external heterogeneity among codes, categories, and potential themes was discussed at regular meetings.

    “The other authors critically read and reviewed the coding results, final synthesis, and interpretation of the themes.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Patton, 2003

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationcitation

    Enthusiasm of technologists for using machines to replace the labor of humans is a threat to the human-in-the-loop.

    “Enthusiasm of technologists for using machines to replace the labor of humans (Brown, 2020) is clearly a threat to the human-in-the-loop.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Brown, 2020 source

      Checked before — its earlier verdict is held back until a new check runs.

    • Rests on an assumption: AI is simply programming and thus not human.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation

    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.

    “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).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Brown, 2020 source

      Checked before — its earlier verdict is held back until a new check runs.

  • Classified as probably not a substantial claim.

    Interpretationcitation

    The fear and possibility of non-detectable academic dishonesty will require substantial efforts to ensure the integrity and social warrant of course grades and academic qualifications.

    “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.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Brown, 2022 source

      Checked before — its earlier verdict is held back until a new check runs.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    This early research is potentially problematic because of its narrowness.

    “This early research, however, is potentially problematic because of its narrowness.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationnone given

    AI creates the possibility that skill with large language models is what students might need in the future.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationreasoning

    The most important challenge centers around assessment and evaluation of learning.

    “The most important challenge centers around assessment and evaluation of learning.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationreasoning

    Generative AIs will force academics to rethink the purpose of assessment, the content and format of what is assessed, the design of assessments, and the formative use of assessed performances.

    “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.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    The challenges associated with meaningful integration of AI into curriculum, instruction, and assessment will remain difficult for years to come.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationnone given

    Several of the trends found in the study are likely to continue for some time to come.

    “While the future remains uncertain, several of the trends found in the study are likely to continue for some time to come.”
    Quote in the paperNot a substantial claim
87 of 87

  • 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.”
5 of 5

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?

  • 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.

    “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]

    The Conclusion describes the review as a first step toward understanding AI’s impact on higher-education curriculum, instruction, and assessment. The ‘Weaknesses in the research’ section calls for researchers from different backgrounds to collaborate on educational uses of AI, but does not propose co-design with educational communities. (LLM, conf=0.85)

    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.

  • New-material theme names an additional study beyond its list of ten

    Minor

    The section says ten studies described this theme and names ten, but it subsequently discusses Zhang et al. (2023) as evidence for an immersive learning environment-one of the attributes assigned to the theme. Zhang et al. is not among the ten named studies.

  • Geographic distribution conflicts with the claim that research was largely WEIRD

    Minor

    The paper says 17 of the 33 studies were conducted in Asia, predominantly China, yet later characterizes the available research as conducted largely in Western (WEIRD) societies. The stated Asian majority does not support that characterization.

2 of 2

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.

  • Cites a retracted work: 10.1111/exsy.12820

    Major

    The cited work was retracted on 2024-12-09 (notice 10.1111/exsy.13810), before this paper was published (2025-04-25). Its authors could have known.

    Cited work 10.1111/exsy.12820 · retracted 2024-12-09 · notice 10.1111/exsy.13810 · this paper published 2025-04-25

  • Cites a retracted work: 10.1155/2023/9281914

    Major

    The cited work was retracted on 2023-12-20 (notice 10.1155/2023/9870608), before this paper was published (2025-04-25). Its authors could have known.

    Cited work 10.1155/2023/9281914 · retracted 2023-12-20 · notice 10.1155/2023/9870608 · this paper published 2025-04-25

  • Cites a retracted work: 10.1007/s00500-023-08119-7

    Major

    The cited work was retracted on 2024-07-22 (notice 10.1007/s00500-024-10002-y), before this paper was published (2025-04-25). Its authors could have known.

    Cited work 10.1007/s00500-023-08119-7 · retracted 2024-07-22 · notice 10.1007/s00500-024-10002-y · this paper published 2025-04-25

3 of 3

Cites

3 retracted

Flagged first, then by citations

Research works printed but not linked: 5

  1. Cuban, 2001, Oversold and Underused: Computers in the Classroom, 10.4159/9780674030107 · DOI
  2. Devi, 2023, Role of artificial intelligence (AI) in sustainable education of higher education institutions in Guwahati City: teacher's perception, Int. Manage. Rev., 111
  3. Patton, 2003, Qualitative evaluation checklist, Eval. Checkl. Proj., 21, 1
  4. Stahlschmidt, 2020, Comparison of web of science, scopus and dimensions databases, KB forschungspoolprojekt, 2020, 37
  5. Tang, 2023, Design and implementation of intelligent evaluation system based on pattern recognition for microteaching skills training, Int. J. Innov. Comput. Inf. Control, 19, 153, 10.24507/ijicic.19.01.153 · DOI

Cited by

Sorted by citations

Related

Authors
Liang, J., Stephens, J. M., & Brown, G. T. L.
Affiliations
University of Auckland (NZ)
Corresponding author
Liang, Jingjing
ORCID
Venue
Frontiers in Education
Publisher
Frontiers Media SA
Published
2025-04-25
Volume
10
ISSN
2504-284X (ISSN-L)
DOI
10.3389/feduc.2025.1522841
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
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
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › 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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-11-06.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-11-06.

Cited sources are checked by gpt-6-sol.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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