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Future research recommendations for transforming higher education with generative AI
Open accessAbstract
Higher education is crucial for producing ethical citizens and professionals globally. The introduction of generative AI (GenAI), such as ChatGPT, has posed opportunities and challenges to the traditional model of education. However, the current conversations primarily focus on policy development and assessment, with limited research on the future of higher education. GenAI's impact on learning outcomes, pedagogy, and assessment is crucial for reforming and advancing the workforce. This qualitative study aims to investigate student perspectives on GenAI's impact on higher education. The study uses an initial conceptual framework driven by a systematic literature review to investigate the opportunities and challenges of AI in education. This framework serves as an initial data collection and analysis framework. A sample of 51 students from three research-intensive universities was selected for this study. Thematic analysis identified three themes and 10 subthemes. The findings suggest that future higher education should be transformed to train students to be future-ready for employment in a society powered by GenAI. They suggest new learning outcomes—skills in learning and teaching with GenAI, AI literacy—and emphasize the significance of interdisciplinarity and maker learning, with assessment focusing on in-class and hands-on activities. They recommend six future research directions – competence for future workforce and its self-assessment measures, AI literacy or competency…
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“AI has prompted instructors to re-think the foundation of assessment, whether the traditional assessment types are still fit for purpose, and ultimately, the purpose of teaching, learning and the very existence of higher education (Chiu, 2024a; Irving-Walton, 2026).”
From Assessment-focused governance mode? Mapping Generative AI policies across 76 UK higher education institutions: Policy framework and recommendations (2026) · cites this paper as Chiu, 2024a
In the citing paper
“It suggests that introducing GenAI can shift the focus from transferring knowledge to processing knowledge and from disciplinary learning to interdisciplinary learning (Chiu, 2024).”
From Higher Education's Generative Artificial Intelligence Paradox: The Meaning of Chatbot Mania (2024) · cites this paper as Chiu, 2024
In the citing paper
“Increasing student con fi dence in utilising GenAI is thus an important consideration regarding student equity (Chiu, 2024; Ng et al., 2021).”
From Artificial Intelligence for Academic Purposes (AIAP): Integrating AI literacy into an EAP module (2025) · cites this paper as Chiu, 2024
In the citing paper
“Understanding how GenAI tools function as well as their capabilities and limitations can help students use these tools responsibly and effectively (Chiu, 2024; Southworth et al., 2023).”
From Artificial Intelligence for Academic Purposes (AIAP): Integrating AI literacy into an EAP module (2025) · cites this paper as Chiu, 2024
In the citing paper
“Meanwhile, whereas universities might have their own GenAI policies, students might not read or understand these documents (Chiu, 2024) or, in other cases, fi nd them lacking and ambiguous (Jisc, 2024).”
From Artificial Intelligence for Academic Purposes (AIAP): Integrating AI literacy into an EAP module (2025) · cites this paper as Chiu, 2024
In the citing paper
“As an important adjunct, scholars have also pointed out the importance of addressing broader societal and ethical considerations of AI (Chiu, 2024; Hornberger et al., 2023).”
From Artificial Intelligence for Academic Purposes (AIAP): Integrating AI literacy into an EAP module (2025) · cites this paper as Chiu, 2024
In the citing paper
“Other studies, however, point to considerable variance in students ' understanding of inappropriate or unethical use of GenAI (Chan, 2023b), again highlighting the need both for clear institutional guidance and policy, as well as explicit pedagogical focus on this area (Chiu, 2024; Kong et al., 2024; Walter, 2024).”
From Artificial Intelligence for Academic Purposes (AIAP): Integrating AI literacy into an EAP module (2025) · cites this paper as Chiu, 2024
In the citing paper
“This is an approach that is in keeping with recent work positioning prompt engineering and pedagogical AI design as central academic competences for the GenAI era (Chiu, 2024; Mollick & Mollick, 2024).”
From Effects of Partial vs. Full Cognitive Offloading with Generative AI on Learning Retention (2026) · cites this paper as Chiu, 2024
Citations
Cites
Using thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414
ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · doi:10.35542/osf.io/5er8f · Cited by 7,552
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326
Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence
Cooper, G. (2023) · Journal of Science Education and Technology · doi:10.1007/s10956-023-10039-y · Cited by 1,263
Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100118 · Cited by 1,181
Self-citationChatGPT for Education and Research: Opportunities, Threats, and Strategies
Rahman, M. M., & Watanobe, Y. (2023) · Applied Sciences · doi:10.3390/app13095783 · Cited by 1,080
Chiu, T. K. F. (2024) · Interactive Learning Environments · doi:10.1080/10494820.2023.2253861 · Cited by 852
Self-citationPeres, R., Schreier, M., Schweidel, D., & Sorescu, A. (2023) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2023.03.001 · Cited by 535
Xia, Q., Chiu, T. K. F., Lee, M., Sanusi, I. T., Dai, Y., & Chai, C. S. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104582 · Cited by 469
Self-citationThirty years of research on online learning
Mayer, R. E. (2018) · Applied Cognitive Psychology · doi:10.1002/acp.3482 · Cited by 428
Research works printed but not linked: 1
- Chiu, 2021, Teacher professional development on Self-determination Theory-based design thinking in STEM education, Educational Technology & Society, 24, 153
Cited by
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“Approach 15 [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59] Experiments 11 [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70] Quantitative Approach (excl.”
Dai, C., Zhang, N., & Liu, M. (2026) · Social Network Analysis and Mining · doi:10.1007/s13278-026-01619-y · Cited by 1
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Abdi, A. S., & Abdullahi, A. M. (2026) · Discover Computing · doi:10.1007/s10791-026-10276-w
Khlaif, Z. N., & Hamamra, B. (2026) · JMIR Medical Education · doi:10.2196/105172
Teng, J., & Cho, S. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2676667
Li, Y., & Zhang, C. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.107196
Noor, F. M., & Salic-Hairulla, M. A. (2026) · Bima Journal of Elementary Education · doi:10.37630/bijee.v4i1.4447
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“Similarly, Chiu emphasizes that when AI tools are used for preliminary drafting, brainstorming, or idea generation, they can enhance cognitive efficiency without compromising critical engagement, provided that learners retain evaluative control over final outputs [25].”
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Sangurima, W. E. J. (2026) · ASCE MAGAZINE · doi:10.70577/asce.v5i2.902
Putra, F. G., & Imama, K. (2026) · AI and Developmental Insights in Education · doi:10.58524/aidie.v2i1.155
Abbasi, F. F., Usman, M., & Latif, B. (2026) · European journal of training and development · doi:10.1108/ejtd-07-2025-0151 · Cited by 1
Acheson, K. (2026) · New Directions for Student Leadership · doi:10.1002/yd.70062
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“…of Generative Arti fi cial Intelligence (GenAI) into higher education undoubtedly presents a transformative shift (Bai ˙ doo-Anu & Ansah, 2023; Chiu, 2024; Cordero et al., 2024; Hsu & Ching, 2023; Kurtz et al., 2024; Samala et al., 2024; Vassileva & Daneva, 2025; Wang et al., 2024; Xia et…”
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“[1] [2].”
Alabdalhaq, T. F. M., ALrousan, D. A., Abualaish, H. S., Alquran, N. M. A., & Alqudah, D. (2026) · EDPACS · doi:10.1080/07366981.2026.2660883
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“38 This is particularly relevant in design studies, where creativity and critical evaluation of visual, textual, and conceptual elements play a central role.”
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“Understanding how students actually use Generative AI in academic tasks has become a critical issue for curriculum design, assessment strategies, and institutional AI governance [15, 17, 18].”
From Skills to Governance: Reconstructing Information Literacy Education in the AIGC Era
Yang, S.-W., & Du, Y. (2026) · 2026 8th International Conference on Computer Science and Technologies in Education (CSTE) · doi:10.1109/cste69562.2026.11649584
“Moving away from static, one-off indicators like "recall and precision," the system comprehensively pivots toward "dynamic process auditing" [27].”
Tikhonova, E. A., & Raitskaya, L. (2026) · Journal of language and Education · doi:10.17323/jle.2026.33523
“Interpretivist / Constructivist / De-sign-based (n=21) Bygstad et al., 2022; Chan & Hu, 2023; Chan & Lee, 2023; Chiu, 2024; Cotton et al., 2024; Cox, 2021; Crawford et al., 2023; Firat, 2023; Heilporn et al., 2021; Jin et al., 2025; Kim et al., 2025; Kohnke et al., 2023; Lee et al., 2024; Lodge et…”
Xue, Z. H. O. U., Chai, Q., Chilukuri, B., & Quach, J. J. Y. (2026) · Journal of University Teaching and Learning Practice · doi:10.53761/pc04tp05 · Cited by 3
“The rise of GenAI has prompted calls to reframe integration strategies beyond opportunistic use (Chiu, 2024b; Shailendra et al., 2024).”
Robles, J. (2026) · QualiSearch Journal of Educational Research and Practice · doi:10.66437/qjerp.v2i4.009
RAG-Powered Backward Curricula Design Software for Integrating Interdisciplinary Learning Objectives
Wani, R. V. (2026) · ACM Symposium on Applied Computing · doi:10.1145/3748522.3785296
Using virtual reality to teach climate change in social studies: a case study in teacher education
ÖZCAN, E., Haşlaman, T., & Yılmaz, G. N. (2026) · Humanities and Social Sciences Communications · doi:10.1057/s41599-026-06835-7 · Cited by 1
Community perspectives on the contextual knowledge of pedagogical development in higher education
Vuojärvi, H., Uusiautti, S., Pruikkonen, A., & Soukka, A. (2026) · Studies in Higher Education · doi:10.1080/03075079.2026.2643787
Jekri, A., Han, C. G. K., & Shaafi, N. F. (2026) · Innovative Teaching and Learning Journal · doi:10.11113/itlj.v10.201
Zimina, O. D., & Константинович, В. В. (2026) · MODERN ADDITIONAL PROFESSIONAL PEDAGOGICAL EDUCATION · doi:10.54884/2414-1186-2026-9-1-028 · Cited by 1
Brindha Devi, S., & Ramnath, R. (2026) · Innovare Journal of Education · doi:10.22159/ijoe.2026v14i2.58384
Revolutionizing sentiment analysis with generative AI: techniques, trends, and challenges
Mamun, R., Sadman, S., Nabil, H. R., Mridha, M. F., & Kabir, M. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21390-8 · Cited by 1
“Chiu et al. [45 ] This study explored students’ views on the effect of generative AI in higher education and emphasized the need for AI skills and interdisciplinary learning to train students for an AI-driven world of work.”
Rojali, A., Idi, A., Karoma, et al. (2026) · Jurnal Ilmu Pendidikan dan Sains Islam Interdisipliner · doi:10.59944/jipsi.v5i1.881
“Selain itu, adaptasi pedagogis diperlukan agar proses pembelajaran tetap relevan dengan perkembangan digital dan kebutuhan mahasiswa kontemporer (Chiu, 2024; Mustafa et al., 2025).”
Exploring Learner Perceptions of AI-Driven Chatbots in a Zimbabwean Higher Education Context
Mushonga, F., & Mangena, A. (2026) · Journal of Computers for Science and Mathematics Learning · doi:10.70232/jcsml.v3i1.52
“This finding is consistent with previous investigations (Chiu, 2024; Kong et al., 2023).”
Teachers’ perceptions of generative AI in gender-inclusive STEM education: a grounded theory study
Tipple, C., Keane, T., Wang, T., & Cerovac, M. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-13908-2 · Cited by 2
“Prior research highlights that developing students’ critical thinking and analytical skills is a prerequisite for engaging with GenAI responsibly (Chiu, 2024 ; Nguyen, 2025).”
Dincer, A. (2026) · Journal of language research · doi:10.51726/jlr.1839281
Hurley, M., Casey, D., Morari, V., & Grimes, D. (2026) · Studies in Higher Education · doi:10.1080/03075079.2026.2614988
Elçiçek, M., & Pesen, A. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2619619
“Today, AI techniques such as computer vision, natural language processing, and machine learning have had a transformative impact not only in industrial fields but also in many academic disciplines, including education (Chiu, 2024; Crompton et al., 2024).”
Guled, A., Akshay, A., Shankar, C. N. U., Anuradha., S., & Thoti, K. K. (2026) · International Journal of Economic Practices and Theories · doi:10.52783/ijept.121 · Cited by 1
Work Engagement: Global Trends, Bibliometric Analysis (2020-2025)
Shah, N. M. (2026) · EDUCATUM Journal of Social Sciences · doi:10.37134/ejoss.vol12.sp.14.2026
Belouettar, A. (2025) · Central European Journal of Educational Research · doi:10.37441/cejer/2025/7/2/15642
“According to Chiu (2024), students are looking forward towards the development of policies regarding AI implementation in Tertiary Education, and this comes as a result of their excitement concerning the development of skills relevant to future job opportunities.”
Conditions for the acceptance of advertising content generated by generative artificial intelligence
Sułkowski, Ł., & Kaczorowska–Spychalska, D. (2025) · Human Technology · doi:10.14254/1795-6889.2025.21-3.8
“Generative artificial intelligence can therefore be defined as an artificial intelligence (AI) system that uses large data sets to create content that did not previously exist (Chiu, 2024), and is therefore innovative and creative in a certain way (Mannuru et al., 2023).”
Taslim, M., Putra, R. P., Daulay, N., & Bulut, S. (2025) · Jurnal Psikologi · doi:10.22146/jpsi.107932 · Cited by 1
“If a student believes cheating is easy and not risky, personal beliefs and social norms become secondary considerations (Chiu, 2024; Tzirides et al., 2024).”
Metadata
- Authors
- Chiu, T. K. F.
- Affiliations
- Chinese University of Hong Kong (HK)
- Corresponding author
- Chiu, Thomas K.F.
- ORCID
- Chiu, Thomas K.F.
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 6
- Pages
- 100197
- ISSN
- 2666-920X (ISSN-L)
- OpenAlex
- W4390278281
- Cited by
- 511
- References
- 49
- Citations by year
- 2026: 207 · 2025: 196 · 2024: 108
- FWCI
- 119.97
- Type
- journal article
- Language
- en
- Update notices
- erratum (2024-06-01)
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Education › Digital Transformation of Higher Education
- Keywords
- generative AI in higher education, student perceptions, AI literacy, AI literacy assessment, interdisciplinary teaching, pedagogy, career readiness, future skills, ChatGPT, research recommendations
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