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The IS discipline and RISE

The Information Systems discipline has, in a remarkably short window (2023–2026), produced a concentrated body of reflective scholarship on how its own knowledge-production process should change when generative AI participates in it. This page synthesizes that body of work, organizes it into three threads, and connects each thread to the projects catalog.

The papers cited below are all indexed in the papers catalog. The synthesis is current as of 2026-05-17; new entries will continue to land in the catalog without necessarily updating this page — treat this as a frozen-in-time map, not a live one.

A short timeline

timeline
    title IS-discipline engagement with GenAI in scholarship
    2023 May : ISR — Susarla, Gopal, Thatcher, Sarker editorial<br/>"The Janus Effect of Generative AI"
    2024 Jan : JAIS Vol 25 Iss 1 — 15-article special-issue cluster<br/>peer review · editorial process · theorizing · KM
    2025     : ISR — Gopal et al. "Inventing with Machines"<br/>EJIS — Mikalef et al. "Responsible AI starts with the artifact"<br/>SSRN — Kumar et al. agentic-AI tradeoff framework<br/>ICIS — Kwon & Yang on productivity & inequality
    2026     : ISR — Abbasi et al. special-issue CFP on GenAI methods of inquiry<br/>EJIS — Ngwenyama, Klein, Rowe "Platform capture"<br/>Acemoglu et al. "AI, Human Cognition and Knowledge Collapse"<br/>arXiv — Jarzębowicz et al. IS landscape synthesis

Three points stand out:

  1. The senior editorial leadership of IS engaged early and explicitly. The 2023 ISR editorial was written by the EIC and three of his closest collaborators (1); the 2024 JAIS Vol 25 Iss 1 cluster includes Atreyi Kankanhalli (JAIS EIC), Ron Weber (former MISQ EIC), Shirley Gregor, and Sirkka Jarvenpaa. This was not a junior-scholar wave — it was a discipline-leadership statement.
  2. JAIS Vol 25 Iss 1 (Jan 2024) is the de facto special issue. Fifteen articles, almost all dealing with how IS knowledge production must change. There is no other concentrated cluster of this size in the discipline.
  3. The 2026 ISR special-issue CFP (2) makes the agenda institutional. It explicitly invites contributions where "GenAI must make a substantive and consequential contribution in the research process." The window for staking out the methodological ground is open now.

Thread 1 — Peer review and the editorial process

The single most-developed sub-area. Five distinct positions are visible in the cluster, and they map directly onto design choices in the catalog's review-focused projects (ape, reviewer, marg).

  • The democratization argument (3). Human–AI peer review can close gaps between scholarly traditions and reduce gatekeeping. The proponents are the senior IS editorial community itself.
  • The two-axis design framework (4). AI-augmented (humans drive, AI assists) vs. AI-driven (AI drives, humans approve) — a 2×2 framework that maps almost one-to-one onto this catalog's autonomy_level scoring for review-focused projects.
  • The human-in-the-loop architecture (5). A specific feasibility/risk analysis of HITL review designs — the most direct architectural reference for any RISE pipeline that embeds a review stage.
  • The RoboReviewer role (6). Weber treats the AI not as an editorial tool but as another reviewer with characteristic strengths and blind spots. This framing is doing silent work in nearly every catalog entry that scores ≥ 1 on internal_evaluation via referee simulation.
  • The journal-policy stance (7, 8). JAIS's editor and an ANU senior scholar lay out the institutional response: what journals do, not what tools do.

A direct critic is also present: 9 in Organization Science argues that AI may exacerbate, not solve, peer review's "more vs better" tradeoff — relevant context for any RISE project that frames AI peer review as unambiguous productivity gain.

For RISE builders: the reviewer and marg projects implement the human-in-the-loop and multi-agent positions, respectively; ape implements something closer to the AI-driven end of 4's two-axis framework. The discipline-level question — whether peer review should be algorithmically scaled at all — is open, and the IS papers above are where it is being argued.

Thread 2 — Theorizing, methodology, and the literature review

The IS discipline's second strong move is to ask whether GenAI changes the epistemic status of scholarly work, not only its volume.

  • Literature reviews as epistemic act (10). Frantz Rowe and Ojelanki Ngwenyama argue that AI-assisted literature reviews change the values of the resulting synthesis, not just its cost. The catalog's literature-synthesis projects (storm, open-scholar, paper-qa, gpt-researcher) answer "how"; this paper asks "with what epistemic consequences."
  • Theorizing with a generative collaborator (11). Sirkka Jarvenpaa (UT Austin) and Stefan Klein (Münster) treat GenAI as a theorizing partner — beyond data analysis, into conceptual contribution. Methodologically ambitious; very few catalog projects currently target this level.
  • Knowledge creation/curation/consumption decomposition (12). Three-part decomposition of where AI fits in the scholarly workflow. Complements the inputs → knowledge production → outputs framing this site uses on the landing diagram.
  • Theory of causal-knowledge analytics (13). Watson, Song, Zhao, and Webster sketch infrastructure for tracking causal claims across the literature — an under-developed knowledge-layer capability noted in Knowledge layer.
  • The KM lens (14). Alavi and Leidner — foundational KM scholars in IS — frame GenAI through the knowledge-management tradition. Establishes the genealogical link from KM to RISE.
  • The "in silico team science" frame (15). Adjacent to IS rather than within it: Li, Saini, Hernandez & Moore (Nature Biotechnology 2026) reframe agentic-AI systems as teams of computational collaborators rather than autonomous tools, organising the design space around three algorithms and seven building-block characteristics. A useful theoretical anchor that complements Jarvenpaa & Klein's theorizing-partner argument with a concrete team-science vocabulary borrowed from the biomedical literature.

The 2025 ISR editorial (16) reads as the institutional successor to all of this: it explicitly invites IS research that uses GenAI as a method of inquiry, not just as a study subject. The 2026 ISR special-issue CFP (2) operationalizes that invitation.

For RISE builders: none of the catalog's end-to-end pipelines yet attempt the kind of theoretical contribution 11 describe. The closest is agent-laboratory, which models literature review and report writing but stops short of theory-building. This is a clear gap.

Thread 3 — Sociotechnical critique

The third thread sits in deliberate tension with the first two. It asks: what are the second-order, field-level, political-economy consequences of routing scholarship through AI?

  • The platform-capture argument (17). The sharpest critical voice in the cluster. Using Marx's theory of subsumption, Ngwenyama, Klein, and Rowe argue that publisher platforms (Elsevier) — reinforced by GenAI — subsume academic labor itself. Every focus: publishing and focus: end-to-end project in the catalog operates inside the conditions this paper describes.
  • The responsible-AI artifact-first argument (18). The EJIS editorial team challenges principles-first responsible-AI frameworks: the artifact's intrinsic characteristics (agentic, autonomous, inscrutable, adaptable) often conflict with the principles. A direct methodological challenge to how RISE systems should be evaluated against governance norms.
  • Knowledge collapse (19). Acemoglu, Kong, and Ozdaglar (MIT) model the macro risk: if humans defer to AI-generated knowledge faster than they verify it, the field's epistemic capital erodes. The field-level evaluation question raised in Evaluation has its strongest theoretical formulation here.
  • Productivity with distributional cost (20). ICIS 2025 paper documenting that the productivity gains documented in 21 are unequally distributed — concentrating in well-resourced researchers. Combined, the two papers sketch a productivity-with- inequality story for RISE deployment.
  • Anthropomorphism (22) and style-engines (23) extend the critique: what looks like AI scholarship is often AI performance of scholarship, and the catalog's projects with high autonomy_level scores are doing both jobs simultaneously.

For RISE builders: if your project sits high on the autonomy_level and lifecycle_coverage dimensions, this thread is the literature you need to engage with — not optionally, because it forms the strongest available case that the work is harmful at the field level. The catalog's evaluation rubric does not yet score field-level risk; this is a candidate dimension for the next rubric version.

Cross-thread observations

The author networks are tight

Several authors recur across threads — most notably Ojelanki Ngwenyama (lit-review epistemics in JAIS, platform capture in EJIS), Frantz Rowe (same), Stefan Klein (theorizing in JAIS, platform capture in EJIS), Dov Te'eni (HITL reviewing, knowledge-process decomposition), Anjana Susarla / Ram Gopal / Jason Bennett Thatcher / Suprateek Sarker (ISR Janus editorial 2023, JAIS democratizing paper 2024, ISR Inventing editorial 2025).

This is a coherent intellectual community doing the work, not a scattered set of one-off engagements. The implication: a credible RISE contribution should engage these authors directly, not work around them.

The geography is broader than the venues suggest

The cluster has strong representation from outside the US: Münster, Galway, Trondheim, Wagga Wagga (ANU), Toronto, Cape Town, Nantes, Israel, Singapore, Hsinchu. The 2026 EJIS papers in particular are non-US-led. RISE positioning that is only US-centric is missing where the discipline is actually thinking.

The empirical work is thin — but no longer absent

The IS-discipline cluster itself remains overwhelmingly conceptual / editorial / position-paper in genre. Empirical IS work on GenAI in research practice exists (20, 21, 24) but is the minority within IS. Outside IS, the empirical situation is shifting fast: 25 (Nature 2026) reports the first agentic system to autonomously discover and wet-lab validate a novel therapeutic candidate, and 15 (Nature Biotechnology 2026) reviews the broader class of biomedical agentic systems through an "in silico team science" lens. The open opportunity for new RISE contributions in IS is to do for the agentic-research-pipeline literature what these Nature-level biomedical papers have begun to do for drug discovery: take the methodological claims of the conceptual cluster and test them empirically against deployed systems.

Reading paths

If you have an hour:

  1. 16 (15 min) — the agenda
  2. 3 (15 min) — the peer-review proposal
  3. 17 (20 min) — the strongest critique
  4. 18 (10 min) — the governance challenge

If you have an afternoon, read the full JAIS Vol 25 Iss 1 — it is short, well-organized, and covers most of the discipline's stake in one place.

If you want the macro / economic frame: 19 + 9 + 21.

If you want the agentic-AI engineering frame (the other side of the same conversation): the catalog's projects index.

  1. Susarla, A., Gopal, R., Thatcher, J. B., & Sarker, S. (2023). The Janus effect of generative AI: Charting the path for responsible conduct of scholarly activities in information systems. Information Systems Research, 34(2). https://doi.org/10.1287/isre.2023.ed.v34.n2 ↩

  2. Abbasi, A. et al. (2026). ISR special issue: Generative AI and new methods of inquiry in information systems research. INFORMS Information Systems Research, Call for Papers. https://pubsonline.informs.org/page/isre/calls-for-papers ↩↩

  3. Sarker, S., Susarla, A., Gopal, R., & Thatcher, J. B. (2024). Democratizing knowledge creation through human-AI collaboration in academic peer review. Journal of the Association for Information Systems, 25(1), 158–171. https://doi.org/10.17705/1jais.00872 ↩↩

  4. Shmueli, G., & Ray, S. (2024). Reimagining the journal editorial process: An AI-augmented versus an AI-driven future. Journal of the Association for Information Systems, 25(1). https://doi.org/10.17705/1jais.00864 ↩↩

  5. Drori, I., & Te’eni, D. (2024). Human-in-the-loop AI reviewing: Feasibility, opportunities, and risks. Journal of the Association for Information Systems, 25(1), 98–109. https://doi.org/10.17705/1jais.00867 ↩

  6. Weber, R. (2024). The other reviewer: RoboReviewer. Journal of the Association for Information Systems, 25(1), 85–97. https://doi.org/10.17705/1jais.00866 ↩

  7. Kankanhalli, A. (2024). Peer review in the age of generative AI. Journal of the Association for Information Systems, 25(1), 76–84. https://doi.org/10.17705/1jais.00865 ↩

  8. Gregor, S. (2024). Responsible artificial intelligence and journal publishing. Journal of the Association for Information Systems, 25(1), 48–60. https://doi.org/10.17705/1jais.00863 ↩

  9. Gartenberg, C., Murray, F., Hasan, S., & Pierce, L. (2026). More versus better: Artificial intelligence, incentives, and the emerging crisis in peer review. Organization Science, 37(3). https://doi.org/10.1287/orsc.2026.ed.v37.n3 ↩↩

  10. Ngwenyama, O., & Rowe, F. (2024). Should we collaborate with AI to conduct literature reviews? Changing epistemic values in a flattening world. Journal of the Association for Information Systems, 25(1), 122–136. https://doi.org/10.17705/1jais.00869 ↩

  11. Jarvenpaa, S., & Klein, S. (2024). New frontiers in information systems theorizing: Human-gAI collaboration. Journal of the Association for Information Systems, 25(1), 110–121. https://doi.org/10.17705/1jais.00868 ↩↩

  12. Schwartz, D., & Te’eni, D. (2024). AI for knowledge creation, curation, and consumption in context. Journal of the Association for Information Systems, 25(1), 37–47. https://doi.org/10.17705/1jais.00862 ↩

  13. Watson, R. T., Song, Y. (April)., Zhao, X., & Webster, J. (2024). Extending the foresight of phillip ein-dor: Causal knowledge analytics. Journal of the Association for Information Systems, 25(1), 145–157. https://doi.org/10.17705/1jais.00871 ↩

  14. Alavi, M., Leidner, D. E., & Mousavi, R. (2024). A knowledge management perspective of generative artificial intelligence. Journal of the Association for Information Systems, 25(1), 1–12. https://doi.org/10.17705/1jais.00859 ↩

  15. Li, B., Saini, A. K., Hernandez, J. G., & Moore, J. H. (2026). Agentic AI and the rise of in silico team science in biomedical research. Nature Biotechnology, 44(5), 711–725. https://doi.org/10.1038/s41587-026-03035-1 ↩↩

  16. Gopal, R. D. et al. (2025). Inventing with machines: Generative AI and the evolving landscape of IS research. Information Systems Research, 36(4), 1949–1967. https://doi.org/10.1287/isre.2025.editorial.v36.n4 ↩↩

  17. Ngwenyama, O., Klein, S., & Rowe, F. (2026). Platform capture of scientific knowledge production: Publishers’ dominance, generative AI and subsumption of academic labor. European Journal of Information Systems, 35(1). https://doi.org/10.1080/0960085X.2026.2642660 ↩↩

  18. Mikalef, P., Benlian, A., Conboy, K., & Tarafdar, M. (2025). Responsible AI starts with the artifact: Challenging the concept of responsible AI in IS research. European Journal of Information Systems, 34(3), 407–414. https://doi.org/10.1080/0960085X.2025.2506875 ↩↩

  19. Acemoglu, D., Kong, D., & Ozdaglar, A. E. (2026). AI, human cognition and knowledge collapse. SSRN Working Paper. https://papers.ssrn.com/sol3/papers.cfm?abstract\id=6326698 ↩↩

  20. Kwon, Y., & Yang, A. (2025). Large language models in academia: Boosting productivity but reinforcing inequality. Proceedings of the International Conference on Information Systems (ICIS). https://aisel.aisnet.org/icis2025/gen\ai/gen\ai/2 ↩↩

  21. Filimonovic, D., Rutzer, C., & Wunsch, C. (2025). Can GenAI improve academic performance? Evidence from the social and behavioral sciences. https://arxiv.org/abs/2510.02408 ↩↩↩

  22. Peter, S., Riemer, K., & West, J. D. (2025). The benefits and dangers of anthropomorphic conversational agents. Proceedings of the National Academy of Sciences, 122(22), e2415898122. https://doi.org/10.1073/pnas.2415898122 ↩

  23. Riemer, K., & Peter, S. (2024). Conceptualizing generative AI as style engines: Application archetypes and implications. International Journal of Information Management, 79, 102824. https://doi.org/10.1016/j.ijinfomgt.2024.102824 ↩

  24. Bapna, R., Bapna, S., Kumar, P., Pamuru, V., Umyarov, A., & Yang, M. (2025). Agentic AI and managers’ analytics capabilities: An exploration. SSRN Working Paper. https://papers.ssrn.com/sol3/papers.cfm?abstract\id=5293722 ↩

  25. Ghareeb, A. E., Chang, B., Mitchener, L., Yiu, A., Szostkiewicz, C. J., Shved, D., Gyimesi, G. J., Laurent, J. M., Wright, S. M., Razzak, M. T., White, A. D., Finnemann, S. C., Hinks, M. M., & Rodriques, S. G. (2026). A multi-agent system for automating scientific discovery. Nature. https://doi.org/10.1038/s41586-026-10652-y ↩