AI, Knowledge Infrastructure, and the Future of Research
A Strategic Workshop with Guided Hands-On Practice
1:00–1:10 p.m. | Welcome and Introductions
Focus
- Welcome remarks from Dr. Shahram Rahimi, Department Head of Computer Science, the Lee J. Styslinger Jr. College of Engineering at The University of Alabama
- Introduction of the workshop facilitator and supporting team
- Overview of the workshop goals, agenda, and expected outcomes
- Brief discussion of how AI is beginning to reshape research across engineering disciplines
Workshop Goal
Participants will explore how AI is moving beyond isolated prompting toward persistent research systems supported by structured knowledge, evidence, provenance, retrieval, and human oversight.
1:10–2:10 p.m. | Strategic Presentation and Live Demonstration
Focus
AI, Knowledge Infrastructure, and the Future of Research
This session provides the conceptual and strategic foundation for the workshop and demonstrates how AI can work with structured research knowledge.
Key Topics
- How AI is changing literature review, synthesis, planning, critique, and research coordination
- Why AI for research is moving beyond chat interfaces and isolated tools
- Research as an end-to-end workflow of connected decisions
- The shift from prompting toward persistent research systems
- The role of memory, schemas, provenance, evidence, benchmarks, and governance
- Why knowledge infrastructure is necessary for reliable AI-supported research
- How agentic AI and AutoResearch build on structured and traceable knowledge
- Implications for individual researchers, laboratories, departments, and institutions
Live Demonstration
The demonstration will present a small AI-assisted research opportunity intelligence system that connects:
- funding solicitations
- faculty profiles
- papers and publications
- institutional research capabilities
- research ideas and potential project teams
Participants will see how research materials can be:
- collected and managed in Obsidian;
- transferred into a structured Markdown-based knowledge environment;
- connected through notes, metadata, links, and provenance;
- accessed by an AI assistant for retrieval, comparison, synthesis, and planning.
Demonstrated Use Cases
- Match funding opportunities with faculty expertise and form potential interdisciplinary teams using evidence from publications and profiles
- Generate research questions and project concepts aligned with solicitation priorities while identifying missing expertise, data, evidence, or facilities
- Produce a concise research opportunity brief that clearly separates documented evidence from AI-generated inference
2:10–2:20 p.m. | Refreshment and Networking Break
A short break for refreshments, informal discussion, and preparation for the hands-on activity.
2:20–3:40 p.m. | Guided Hands-On Build Session
Project: Building a Personal Knowledge System for Research with AI-Assisted Note Organization and Retrieval
Objective: Participants will work with a prepared collection of grant solicitations, faculty profiles, and selected publications to explore how structured knowledge can support research opportunity discovery, team formation, and project ideation.
3:40–4:00 p.m. | Show and Tell, Evaluation, and Leadership Discussion
Objective
Participants will share selected outputs from the hands-on activity and discuss how similar systems could support research within their own departments and institutions.
Activities
- Selected groups present their research opportunity briefs
- Participants compare different faculty-matching and team-formation strategies
- AI-assisted outputs are evaluated for evidence, relevance, transparency, and practical value
- Discussion of what worked, what remained uncertain, and where human judgment was required
Leadership Discussion
- How could personal knowledge systems evolve into laboratory or department-level infrastructure?
- What research knowledge should remain personal, shared with a team, or managed institutionally?
- How should universities address privacy, intellectual property, provenance, and responsible AI use?
- What infrastructure and training will faculty and students need?
- How might these systems support interdisciplinary collaboration and proposal development?
- How should academic leaders prepare for increasingly agentic research environments?
Closing Takeaway
AI becomes more useful when it can work with knowledge that is structured, persistent, traceable, and connected to evidence. The future of research will depend on how effectively researchers and institutions combine AI capabilities with strong knowledge infrastructure and human judgment.