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What is Research Information Systems Engineering?

Working definition

Research Information Systems Engineering (RISE) is the discipline concerned with the design, construction, and study of information systems whose primary purpose is to produce scholarly knowledge.

A RISE system is distinguished from neighboring artifacts by three commitments:

  1. Knowledge production as output. The system's terminal artifacts are scholarly: papers, datasets, code releases, replication reports — not predictions, decisions, or recommendations as ends in themselves.
  2. Agentic mediation. The pipeline is at least partially executed by AI agents — LLMs with tools, multi-agent orchestrations, or human–AI hybrids — not solely by human researchers using non-agentic software.
  3. Methodological accountability. The system is designed against research-methodological norms (identification, replicability, peer-review readiness, citation grounding), not only against software-engineering norms.

The three commitments are jointly necessary. A system that produces scholarly outputs without agentic mediation is a research workflow, not RISE. An agentic system whose terminal artifact is a chat response is a chatbot, not RISE. An agentic system that drafts papers but does not engage methodological standards (no identification, no citation grounding, no review) is a style engine — see 1 — useful, but not RISE.

Why a separate name?

The umbrella terms in use — AI for science, AI scientist, agentic research, autonomous research agents — each foreground one face of the object. AI for science is too broad: it includes AlphaFold-style prediction systems whose outputs are scientific claims about the world rather than scholarly artifacts. AI scientist anthropomorphizes a system as a researcher, which prejudges the sociotechnical question (cf. 2). Agentic research describes the method but underspecifies the output and accountability dimensions.

RISE names the engineering and methodological discipline that sits across these — borrowing from information-systems design science, e-Science research infrastructure, and AI4Science — and treats the agentic research pipeline itself as its primary object of design and study.

What RISE is not

  • Generic AI-for-science tooling (e.g., protein-structure prediction) that produces scientific predictions rather than scholarly artifacts.
  • Pure information-systems theory without a built artifact.
  • AI writing assistants whose unit of output is prose rather than research products.
  • Evaluation infrastructure for AI systems (e.g., aviary) — directly relevant, but not a RISE pipeline.

Scope of this knowledge base

RISE here is treated as a named subfield, not a synonym for "AI for science." The literature surveyed (see Papers) foregrounds the agentic pipeline as the object of design and study. The projects catalog evaluates concrete systems — both owned (E2ER) and external (Sakana AI Scientist, STORM, GPT Researcher, PaperQA2, OpenScholar, MARG, Agent Laboratory, Robin, APE, Reviewer, …) — against a shared rubric.

Sociotechnical commitment

This knowledge base sits, deliberately, in the sociotechnical tradition of information systems research 3. A RISE contribution is not only a technical artifact: it is an intervention in the social system of scholarship. That commitment shapes the evaluation rubric (autonomy level and human-in-loop are first-class), shapes the scope (peer-review tools are first-class projects, not peripheral), and shapes the kinds of papers featured in the literature (the discipline's reception of GenAI is as central as its mechanics).

  • e-Science and research infrastructure. Cyberinfrastructure for large-scale, data-intensive science.
  • AI4Science. Broader — includes scientific prediction (AlphaFold) alongside scholarly-artifact production.
  • Information Systems (design-science tradition). The methodological home of artifact-producing research.
  • Open and reproducible science. Pre-registration, replication, computational reproducibility.
  • Science of science / metascience. Empirical study of how research is produced and disseminated.

See History for the lineages these draw on.

  1. 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 ↩

  2. 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 ↩

  3. Sarker, S. et al. (2019). The sociotechnical axis of cohesion for the IS discipline: Its historical legacy and its continued relevance. MIS Quarterly, 43(3), 695–719. https://doi.org/10.25300/misq/2019/13747 ↩