Student’s Perception of Syllabus Adoption in Higher Education; Redesigning for the Age of AI

Authors

DOI:

https://doi.org/10.70232/jrep.v2i2.61

Keywords:

Artificial Intelligence, Higher Education, Syllabus, Student Perception

Abstract

This research investigates the incorporation of artificial intelligence (AI) within higher education curricula, concentrating on students’ views regarding necessary syllabus modifications to adequately prepare for a future influenced by AI. The study delineates three primary objectives: to determine what elements should be included in the syllabus, to identify key AI tools for academic growth, and to understand the challenges associated with embedding AI into current educational frameworks. A qualitative research study was adopted, employing semi-structured interviews with ten students selected through purposive sampling. A thematic analysis was performed to uncover recurring themes and insights. The results reveal a significant demand for AI literacy, project-based learning, and practical applications as vital components of a restructured syllabus. Students identified tools such as Grammarly, ChatGPT, and specialized AI platforms for various disciplines as essential for academic achievement. Nonetheless, the challenges of over-reliance on AI, diminished creativity, and complacency were also noted. This investigation highlights the necessity for a syllabus that effectively balances technological integration with the maintenance of creativity and critical thinking. By considering student perspectives, higher education institutions can enhance readiness for graduates poised to enter an AI-influenced job market. The implications of this study suggest that educators should emphasize the inclusion of project-based learning modules that motivate students to leverage AI tools for addressing real-world challenges. Faculty development programs should aim at providing educators with the competencies required to assist students in the ethical and productive application of AI. Future inquiries should examine faculty opinions and the long-term effects of AI-enhanced syllabi.

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References

Akgun, S., & Greenhow, C. (2021). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2, 431-440. https://doi.org/10.1007/s43681-021-00096-7

Adipat, S. (2024). Advancing higher education with the transition to smart universities: a focus on technology. Shanlax International Journal of Education, 12(3), 29-36. https://doi.org/10.34293/education.v12i3.7635

Cui, W., Xue, Z., & Thai, K. P. (2019). Performance Comparison of an AI-Based Adaptive Learning System in China. Proceedings 2018 Chinese Automation Congress, CAC 2018.

Crompton, H., Burke, D. (2023). Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20, 22. https://doi.org/10.1186/s41239-023-00392-8

Baron, K. (2017). Changing to concept-based curricula: the process for nurse educators. The Open Nursing Journal, 11(1), 277-287. https://doi.org/10.2174/1874434601711010277

Birla, R. (2023). The challenges of media education in the digital era. Journal of Communication and Management, 2(4), 281-288. https://doi.org/10.58966/jcm20232411

Bruehl, M., Pan, D., & Ferrer‐Vinent, I. (2014). Demystifying the chemistry literature: building information literacy in first-year chemistry students through student-centered learning and experiment design. Journal of Chemical Education, 92(1), 52-57. https://doi.org/10.1021/ed500412z

Chan, C., & Hu, W. (2023). Students’ voices on generative AI: perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20.

Chetty, K. (2023). Ai literacy for an ageing workforce: leveraging the experience of older workers. Obm Geriatrics, 7(3), 1-17. https://doi.org/10.21926/obm.geriatr.2303243

Chiu, T., Meng, H., Chai, C., King, I., Wong, S., & Yam, Y. (2022). Creation and evaluation of a pretertiary artificial intelligence (AI) curriculum. IEEE Transactions on Education, 65(1), 30-39. https://doi.org/10.1109/te.2021.3085878

Chin, C., Munip, H., Miyadera, R., Thoe, N., Ch’ng, Y., & Promsing, N. (2018). Promoting education for sustainable development in teacher education integrating blended learning and digital tools: an evaluation with exemplary cases. Eurasia Journal of Mathematics Science and Technology Education, 15(1). https://doi.org/10.29333/ejmste/99513

Dishon, G. (2017). New data, old tensions: Big data, personalized learning, and the challenges of progressive education. Theory and Research in Education

Gligorea, I., Cioca, M., Oancea, R., Gorski, A.-T., Gorski, H., & Tudorache, P. (2023). Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review. Education Sciences, 13(12), 1216.

Halimi, M., Rahmat, R., Nugraha, R., & Pratiwi, E. (2022). Young digital citizen answers: can online learning improve the quality of civic education learning?. Jurnal Civics Media Kajian Kewarganegaraan, 19(1), 99-109. https://doi.org/10.21831/jc.v19i1.40140

Huang, J., Saleh, S., & Liu, Y. (2021). A review on artificial intelligence in education. Academic Journal of Interdisciplinary Studies, 10(3), 206-214. https://doi.org/10.36941/ajis-2021-0077

Iberahim, A., Yunus, M., & Sulaiman, N. (2023). A review on technology-enhanced language learning (tell). International Journal of Academic Research in Business and Social Sciences, 13(2). https://doi.org/10.6007/ijarbss/v13-i2/16496

Kohli, V. & Dhaliwal, U. (2013). Medical students’ perception of the educational environment in a medical college in India: a cross-sectional study using the Dundee ready education environment questionnaire. Journal of Educational Evaluation for Health Professions, 10, 5. https://doi.org/10.3352/jeehp.2013.10.5

Kakish, K., & Pollacia, L. (2018). Adaptive learning to improve student success and instructor efficiency in introductory computing courses. Proceedings of the 34th Information Systems Education Conference, ISECON 2018.

Kalnina,D., Namante, D., Baranova, S. (2024). Artificial Intelligence for higher education benefits and challenges for preservice teachers. https://doi.org/10.3389/feduc.2024.1501819

Koval, K. (2023). Competencies and technologies for improving distance professional education: a modern perspective and approach. Sciencerise Pedagogical Education, 3(54), 27-31. https://doi.org/10.15587/2519-4984.2023.282682

Kulik, J & Fletcher, J. (2016). Effectiveness of Intelligent Tutoring Systems: A Meta-Analytic Review. Review of Educational Research, 86(1) 42-78

Larsson, S. (2023). Four facets of AI transparency., 445-455. https://doi.org/10.4337/9781803928562.00047

Laupichler, M. (2023). Evaluating AI courses: a valid and reliable instrument for assessing artificial-intelligence learning through comparative self-assessment. Education Sciences, 13(10), 978. https://doi.org/10.3390/educsci13100978

Lee, H., Kang, Y., Lee, S., Lin, Y., Kim, D., & Ihm, J. (2023). Relationship matters: a qualitative study of medical students’ experiences in a learner-driven research program in South Korea. BMC Medical Education, 23(1). https://doi.org/10.1186/s12909-023-04337-7

Mayes, R., Natividad, G., & Spector, J. (2015). Challenges for educational technologists in the 21st century. Education Sciences, 5(3), 221-237. https://doi.org/10.3390/educsci5030221

Mazarakis, A. (2023). What is critical for human-centered AI at work? – toward an interdisciplinary theory. Frontiers in Artificial Intelligence, 6. https://doi.org/10.3389/frai.2023.1257057

Meyer, K. (2023). How to explain AI in an understandable way? citizen scientists, AI experts, and artists find answers together. https://doi.org/10.22323/1.442.0010

McLaughlin, J., Wolcott, M., Hubbard, D., Umstead, K., & Rider, T. (2019). A qualitative review of the design thinking framework in health professions education. BMC Medical Education, 19(1). https://doi.org/10.1186/s12909-019-1528-8

Mnguni, L. (2019). Exploring the student and social accountability of the life sciences curriculum: a case of HIV/aids. Problems of Education in the 21st Century, 77(3), 410-423. https://doi.org/10.33225/pec/19.77.410

Murad, H. (2023). Implementation of problem-based learning (pbl) in the digital era in higher education of Iraq. International Journal of Information Technology and Computer Engineering, 35, 1-12. https://doi.org/10.55529/ijitc.35.1.12

Nye, B. D. (2015). Intelligent tutoring systems by and for the developing world: a review of trends and approaches for educational technology in a global context. International Journal of Artificial Intelligence in Education, 25(2), 177-203.

Ng, D. (2023). Design and validation of the AI literacy questionnaire: the affective, behavioral, cognitive and ethical approach. British Journal of Educational Technology, 55(3), 1082-1104. https://doi.org/10.1111/bjet.13411

Ng, D., Leung, J., Chu, K., & Qiao, M. (2021). ai literacy: definition, teaching, evaluation, and ethical issues. Proceedings of the Association for Information Science and Technology, 58(1), 504-509. https://doi.org/10.1002/pra2.487

Otero, L., Catalá, A., Morante, M., Taboada, M., López, B., & Barro, S. (2023). Ai literacy in k-12: a systematic literature review. International Journal of Stem Education, 10(1). https://doi.org/10.1186/s40594-023-00418-7

Parveen, D. (2024). The role of digital technologies in education: benefits and challenges. Int Res J Adv Engg Mgt, 2(06), 2029-2037. https://doi.org/10.47392/irjaem.2024.0299

Pattacini, L. (2018). Experiential learning: the field study trip, a student-centered curriculum. Compass Journal of Learning and Teaching, 11(2). https://doi.org/10.21100/compass.v11i2.815

Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 22. https://doi.org/10.1186/s41039-017-0062-8

Putri, R., Gusnedi, G., Desnita, D., & Dewi, W. (2023). Effect of the problem-based learning model with concept map on physics students achievement. Department of Physics Universitas Negeri Padang, 1(1), 36-42. https://doi.org/10.24036/ple.v1i1.13

Qureshi, M., Khan, N., Raza, H., Imran, A., & Ismail, F. (2021). Digital technologies in education 4.0. Does it enhance the effectiveness of learning? a systematic literature review. International Journal of Interactive Mobile Technologies, 15(4), 31. https://doi.org/10.3991/ijim.v15i04.20291

Qi, S., Li, S., & Zhang, J. (2021). Designing a teaching assistant system for physical education using web technology. Mobile Information Systems, 1-11. https://doi.org/10.1155/2021/2301411

Relmasira, S. (2023). Fostering AI literacy in elementary science, technology, engineering, art, and mathematics (steam) education in the age of generative AI. Sustainability, 15(18), 13595. https://doi.org/10.3390/su151813595

Rütti-Joy, O. (2023). Building AI literacy for sustainable teacher education. Zeitschrift Für Hochschulentwicklung, 18(4), 175-189. https://doi.org/10.21240/zfhe/18-04/10

Sit, C., Srinivasan, R., Amlani, A., Muthuswamy, K., Azam, A., Monzon, L., … & Poon, D. (2020). Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights Into Imaging, 11(1). https://doi.org/10.1186/s13244-019-0830

Singh, V., Hiran, K, K. (2022). The impact of AI on teaching and learning in higher education technology. Journal of Higher Education Theory and Practice, 22(13). https://doi.org/10.33423/jhetp.v22i13.5514

Sun, J. (2018). Development strategy of dance education in a digital era. Educational Sciences Theory & Practice. https://doi.org/10.12738/estp.2018.6.255

Stewart, J. (2023). Western Australian medical students’ attitudes towards artificial intelligence in healthcare. Plos One, 18(8), e0290642. https://doi.org/10.1371/journal.pone.0290642

Sholjakova, M. (2019). Technological advances in medical education in intensive care. Anaesthesia Pain & Intensive Care, 119-123. https://doi.org/10.35975/apic.v23i2.1054

Sumarno, S. (2023). Integration of digital technology in public management transformation:. International Journal of Asian Education, 4(2), 115-120. https://doi.org/10.46966/ijae.v4i2.348

Wang, S., Wang, F, Zhu, Z., Wang, J., Tran, T., Du, Z (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications

Yetişensoy, O. & Rapoport, A. (2023). Artificial intelligence literacy teaching in social studies education. Journal of Pedagogical Research. https://doi.org/10.33902/jpr.202320866

Zaman, B. (2023). Transforming Education Through AI, Benefits, Risks, and Ethical Considerations.

Zhai, X., ChatGPT User Experience: Implications for Education (December 27, 2022). Available at SSRN: https://ssrn.com/abstract=4312418 or http://dx.doi.org/10.2139/ssrn.4312418

Zhao, L., Wu, X., & Luo, H. (2022). Developing ai literacy for primary and middle school teachers in china: based on a structural equation modeling analysis. Sustainability, 14(21), 14549. https://doi.org/10.3390/su142114549

Zhang, H., Lee, I., Ali, S., DiPaola, D., Cheng, Y., & Breazeal, C. (2022). Integrating ethics and career futures with technical learning to promote ai literacy for middle school students: an exploratory study. International Journal of Artificial Intelligence in Education, 33(2), 290-324. https://doi.org/10.1007/s40593-022-00293-

Zhang, Y. (2022). Developing efl teachers’ technological pedagogical knowledge through practices in virtual platform. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.916060

Zheng, J., Williams-Livingston, A., Danavall, N., Ervin, C., & McCray, G. (2021). Online high school community health worker curriculum: key strategies of transforming, engagement, and implementation. Frontiers in Public Health, 9. https://doi.org/10.3389/fpubh.2021.667840

Zhou, X. & Lin, P. (2020). Designing ai learning experiences for k-12: emerging works, future opportunities and a design framework. https://doi.org/10.48550/arxiv.2009.10228

Zhou, J. & Zhao, Z. (2018). Research on e-commerce curriculum reform in the age of mobile internet. https://doi.org/10.25236/icepms.2018.099

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Published

2025-05-02

How to Cite

Mosae, T., & Kaushal, R. (2025). Student’s Perception of Syllabus Adoption in Higher Education; Redesigning for the Age of AI. Journal of Research in Education and Pedagogy, 2(2), 284-295. https://doi.org/10.70232/jrep.v2i2.61

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