Research agenda for generative AI in teaching and learning: identify needed skills, resources and capabilities¶

Strategy · All strategies
Evidence · 5 claims (5 for) · 3 studies (2 review, 1 theoretical), q1–q2 · 0 of 3 report an effect size · 5 claims rest on one study

Description¶

Across the expert contributions the article identifies questions for further research in three thematic areas, including teaching, learning, and scholarly research. A concrete strand asks, from a skills view, "which skills, resources and capabilities (both technical and non-technical) are necessary within organisations to better understand the limitations of ChatGPT within different contexts of use, and promote ethical decision-making". It further asks what role government policies, training providers, higher education and technology developers should play in developing these skills among a workforce whose jobs may be made redundant by robotic process automation.

Design Implications¶

Context¶

Requirements¶

• Multi-disciplinary research examining ethical concerns surrounding black-box algorithms

Constraints¶

• The agenda is a set of proposed research questions, not tested findings

Target Learners¶

• Higher education students, educators, researchers and the human workforce

Target Learning Goals¶

• Understanding limitations of generative AI in different contexts of use
• Ethical decision-making with generative AI

Claims¶

• The skills required in a world powered by ChatGPT and related technologies will be different from those demanded previously [+W]
• Generative AI platforms cannot yet independently lead meaningful innovation; at best they augment human intelligence for innovation [+W]
• The review identifies three pathways of AI-empowered STEM education: technological leap to generative AI, integration into instructional systems, and value shift toward competency cultivation [+M]
• AI's key contribution in STEM education is intelligent scaffolding that lowers the threshold for understanding knowledge, driving a shift from knowledge transmission to capability development [+W]
• AI-supported design learning demands integrative capabilities, judgement, and learner agency rather than a single new skill [+W]

Related Strategies¶

• Prioritize prospective, in-curriculum evaluations of AI governance interventions over additional isolated benchmark studies
• Prioritize a research agenda on the long-term effects, optimal parameters, and trauma-skill tradeoffs of lockdown drills

Examples¶

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Key Sources¶

• Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., Carter, L., Chowdhury, S., Crick, T., Cunningham, S. W., Davies, G. H., Davison, R. M., Dé, R., Dennehy, D., Duan, Y., Dubey, R., Dwivedi, R., Edwards, J. S., Flavián, C., Gauld, R., Grover, V., Hu, M.-C., Janssen, M., Jones, P., Junglas, I., Khorana, S., Kraus, S., Larsen, K. R., Latreille, P., Laumer, S., Malik, F. T., Mardani, A., Mariani, M., Mithas, S., Mogaji, E., Nord, J. H., O'Connor, S., Okumus, F., Pagani, M., Pandey, N., Papagiannidis, S., Pappas, I. O., Pathak, N., Pries-Heje, J., Raman, R., Rana, N. P., Rehm, S.-V., Ribeiro-Navarrete, S., Richter, A., Rowe, F., Sarker, S., Stahl, B. C., Tiwari, M. K., van der Aalst, W., Venkatesh, V., Viglia, G., Wade, M., Walton, P., Wirtz, J. and Wright, R. (2023) "So what if ChatGPT wrote it?" Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71. https://doi.org/10.1016/j.ijinfomgt.2023.102642