LAM Siew Hong, and Ian CHAN Zhi Wen
Department of Biological Sciences, Faculty of Science (FoS), National University of Singapore (NUS)
Sub-Theme
Building Technological and Community Relationships
Keywords
Generative artificial intelligence, Bloom’s Taxonomy, academic behavioural confidence, metacognitive thinking, student learning
Category
Paper Presentation
Generative Artificial Intelligence (GenAI), with its powerful and diverse applications, has profoundly changed the way educators teach (Chiu, 2024) and students learn (Chan & Hu, 2023) in higher education. This impact is reflected in the amount of research being conducted in this area—a quick search in Google Scholar for “Generative AI” and “higher education” showed 962 journal articles and book chapters published in English in 2024 alone. In general, students tend to have a positive attitude towards the opportunity to use GenAI to support learning activities such as writing, research, and analysis (Chan & Hu, 2023) in a manner that is both personalised and interactive. On the other hand, educators tend to weigh the above-mentioned opportunities against various concerns, including a degradation of students’ ability to think independently, creatively, and critically, and a decline in academic integrity due to plagiarism (Michel-Vallarrea et al., 2023).
In the past year, data have emerged to support these concerns. Abbas et al. (2024) show that university students who use GenAI more frequently tend to also procrastinate more, have poorer memory, and perform worse academically. Recent pre-published data released by Kosmyna et al. (2025; on the preprint server arxiv.org) suggest that students who use GenAI to assist in essay writing may display weaker connectivity in brain activity and perform worse neurally, linguistically, and behaviourally. However, a systematic review by Wang & Fan (2025) showed different results for different contexts—e.g., improving writing creativity and efficiency (Imran & Almusharraf, 2023) but also hindering the development of critical thinking skills (Khurma et al., 2024). Given its pervasive use—92% of higher education students in the UK use GenAI in some form (Freeman, 2025) and trends are likely to be similar in Singapore—there is an urgent need to determine (1) whether the use of GenAI in our academic settings is harming or benefitting our students; and (2) which aspects of GenAI usage is affecting them.
To shed some light and obtain insights into these questions, we conducted a survey of 382 students from the National University of Singapore. The 42-item survey, divided into six sections, provided information on the students:
- GenAI usage frequency—duration spent using GenAI for their academic work;
- GenAI usage type—what academic activities they used GenAI for;
- GenAI usage confidence for lower to higher order thinking tasks based on the revised Bloom’s taxonomy (Krathwohl, 2002);
- Metacognitive strategic thinking—their awareness and ability to regulate their own cognitive processes to overcome challenges in learning or achieve academic goals (Chen et al. 2020);
- Academic confidence and performance—in terms of Academic Behavioural Confidence (Sanders & Sanders, 2009) and their cumulative grade point average; and
- Demographic information—gender, age, degree programme, and candidature year.
Our analysis suggests that contrary to recent studies, increased GenAI usage frequency and usage type did not generally result in poorer or better academic performance. On the other hand, increased metacognitive strategic thinking to overcome academic challenges or achieve academic goals may contribute to increased confidence in the usage of GenAI tools for both lower and higher-order thinking tasks. Importantly, coupling frequent metacognitive thinking with greater confidence in GenAI usage from lower to higher order thinking tasks may improve student academic performance through increased Academic Behavioural Confidence. Our findings suggest that educators in higher education need to equip students with the right skills (to perform lower to higher order thinking tasks) and mindsets (e.g., metacognitive and ethical thinking) to utilise GenAI in a cognitively-challenging manner, as this will help students to benefit from GenAI usage.
References
Abbas, M., Jam, F. A., & Khan, T. I. (2024). Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. International Journal of Educational Technology in Higher Education, 21(1), 10-22. https://doi.org/10.1186/s41239-024-00444-7
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 1-18. https://doi.org/10.1186/s41239-023-00411-8
Chen, P., Powers, J. T., Katragadda, K. R., Cohen, G. L., & Dweck, C. S. (2020). A strategic mindset: An orientation toward strategic behavior during goal pursuit. Proceedings of the National Academy of Sciences, 117 (25), 14066-14072. https://doi.org/10.1073/pnas.2002529117
Chiu, T. K. F. (2024). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 6, 100197. https://doi.org/10.1016/j.caeai.2023.100197
Freeman, J. (2025). Student generative AI survey 2025. Higher Education Policy Institute: London, UK.
Imran, M., & Almusharraf, N. (2023). Analyzing the role of ChatGPT as a writing assistant at higher education level: A systematic review of the literature. Contemporary Educational Technology, 15(4), ep464. https://doi.org/10.30935/cedtech/13605
Khurma, O. A., Albahti, F., Ali, N., & Bustanji, A. (2024). AI ChatGPT and student engagement: Unraveling dimensions through PRISMA analysis for enhanced learning experiences. Contemporary Educational Technology, 16(2), ep503. https://doi.org/10.30935/cedtech/14334
Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X-H., Beresnitzky, A. V., Braunstein, I. & Maes, P. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an ai assistant for essay writing task. arXiv. https://doi.org/10.48550/arXiv.2506.08872
Krathwohl, D. R. (2002) A revision of bloom’s taxonomy: An overview. Theory Into Practice, 41(4), 212-218. https://doi.org/10.1207/s15430421tip4104_2
Michel-Villarreal, R., Vilalta-Perdomo, E., Salinas-Navarro, D. E., Thierry-Aguilera, R., & Gerardou, F. S. (2023). Challenges and opportunities of generative AI for higher education as explained by ChatGPT. Education Sciences, 13(9), 856. https://doi.org/10.3390/educsci13090856
Sander, P., & Sanders, L. (2009). Measuring academic behavioural confidence: The ABC scale revisited. Studies in Higher Education, 34(1), 19-35. https://doi.org/10.1080/03075070802457058
Wang, J., & Fan, W. (2025). The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: Insights from a meta-analysis. Humanities and Social Sciences Communications, 12, 1-21, Article 621. https://doi.org/10.1057/s41599-025-04787-y