Major update to my recommended scholarly reading list on gen AI 𝗶𝗻 academic research (not AI as the subject of research itself, but using AI to support / enhance - and social science research primarily, apologies to STEMMers but I can't credibly recommend specialist work in those fields!) which I initially did in early 2024. Even most of these have lots of out of date info but that's academic research + peer review timelines for you, no chance of keeping up with the pace of AI developments. This list isn't for cutting edge insights anyway, it's a gateway for those in HE who view any AI info not published by major scholarly publishers as nothing but 'tech bro / CEO hype' 😊 -- 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄𝘀 𝗮𝗻𝗱 𝗶𝗻𝘁𝗿𝗼𝘀 Bail, C. A. (2024). Can Generative AI improve social science?, Proc. Natl. Acad. Sci. U.S.A. 121 (21). https://lnkd.in/eecE_Kxy Korinek, A. (2024). Generative AI for economic research: LLMs learn to collaborate and reason. NBER Working Paper. https://lnkd.in/eb2FRUq9 Perkins, M. and Roe, J. (2024). Generative AI tools in academic research: Applications and implications for qualitative and quantitative research methodologies. arXiv preprint arXiv:2408.06872. https://lnkd.in/eWuJ8mVT Xu, R. et al. (2024). AI for social science and social science of AI: A survey. Information processing and management, 61(3). https://lnkd.in/exDB5QrV 𝗘𝘁𝗵𝗶𝗰𝘀 𝗮𝗻𝗱 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝘂𝘀𝗲 Al-kfairy, M. et al. (2024). Ethical Challenges and Solutions of Generative AI: An Interdisciplinary Perspective. Informatics, 11(3), 58. https://lnkd.in/e6zmTJV6 EU Commission. (2025) . Living guidelines on the responsible use of generative AI in research. (2nd Edition) https://lnkd.in/eTA6TakA Rana, N. K. (2024). Generative AI and Academic Research: A Review of the Policies from Selected HEIs. Higher Education for the Future, 12(1), 97-113. https://lnkd.in/e8PnR8_F 𝗚𝗲𝗻 𝗔𝗜 𝗮𝘀 𝗠𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝘆 Davidson, T., & Karell, D. (2025). Integrating Generative Artificial Intelligence into Social Science Research: Measurement, Prompting, and Simulation. Sociological Methods & Research, 54(3), 775-793. https://lnkd.in/exJKXJtt Manning, B. S., Zhu, K., & Horton, J. J. (2024). Automated social science: Language models as scientist and subjects. (working paper). Massachusetts Institute of Technology and Harvard University. https://lnkd.in/e-2-Yy8b Than, N. et al. (2025). Updating “The Future of Coding”: Qualitative Coding with Generative Large Language Models. Sociological Methods & Research, 54(3), 849-888. https://lnkd.in/eFpFsM-x Zhang, Z. et al. (2024). Simulating classroom education with LLM-empowered agents (arXiv:2406.19226). arXiv. https://lnkd.in/eqg8WbyN
This one really caught my attention, especially with my background in software engineering, where “coding” has a very different meaning and history. What I found valuable in Than et al. (2025) is how it shows LLMs can genuinely support qualitative research, not by replacing human interpretation, but by helping with that initial, often laborious coding effort. It’s also encouraging to see open models like Llama 3 and Gemma getting close to GPT-4. That really matters in terms of access, reproducibility, and long-term trust in research pipelines. That said, it also raised a deeper question for me: if we think of LLMs as junior researchers today, how do they ever become senior? In human teams, juniors learn not just through repetition, but through mentorship, shared norms, and exposure to ambiguity. They absorb the culture, ethics, and grey areas of the work over time. That’s a hard thing to automate. Appreciate you curating this list, Lee 👏 .
Thanks for the great share, Lee Mager, PhD!
Professor, Evidence Synthesis Methodologist, & AI Research Librarian
6dI wonder why do you limit your list to generative AI? Text mining and other analytical techniques possibly by other AI tools.