Role-Based AI Training Interests · Cross-Role Overlap Analysis · UTHSC ITS
Staff show the strongest interest in using AI to automate routine tasks, streamline workflows, and reduce manual administrative burden. This reflects a clear demand for practical, day-one applicable AI skills.
High interest in using AI for drafting emails, documents, and reports. Staff want tools that improve communication quality and reduce time spent on writing tasks.
Strong appetite for training on using AI to analyze data, create summaries, and generate actionable insights from operational and survey data.
IT Staff specifically request training on governance, data privacy, and responsible AI deployment — consistent with their oversight and policy roles.
Both staff and IT staff want to learn how to write effective prompts to get better results from AI tools like CoPilot — the most used UT-provided platform.
IT staff are uniquely interested in training around selecting, evaluating, and deploying AI tools enterprise-wide — a capability gap that could be addressed through a dedicated IT-AI track.
Students' top training priority is using AI to accelerate literature discovery, synthesize papers, and support academic research — directly aligned with their academic workloads.
Strong interest in AI tools that help with academic writing, structuring arguments, and formatting citations correctly — while remaining academically ethical.
Students want to use AI to summarize lecture content, create study guides, and generate practice questions — directly improving learning efficiency.
Students express a clear need to understand the boundaries of appropriate AI use in academic work — a topic that requires institutional guidance and structured training.
Growing interest in AI skills as career differentiators — including how to articulate AI proficiency for healthcare and research career paths.
Students want foundational training on how to effectively use available AI tools — starting with prompt construction and understanding tool capabilities and limitations.
Faculty's top priority is training on how to use AI to build better courses — including AI-generated syllabi, rubrics, case studies, and adaptive learning materials.
Faculty want training on using AI to support literature reviews, draft grant proposals, and analyze research data — accelerating scholarly output.
Interest in AI tools that can assist with providing formative feedback, grading rubric application, and personalizing student responses at scale.
Faculty want guidance on setting AI-use policies for their courses, detecting AI-generated submissions, and communicating standards to students clearly.
Interest in AI that supports lecture preparation — including slide generation, transcription, and creating engaging multi-modal instructional content.
Faculty in health sciences want specialized training on AI's role in clinical education, simulation support, and patient case scenario generation.
Every role group — staff, students, and faculty — identifies ethical AI use and responsible deployment as a training need. A single cross-role foundational course could address this universally.
All three groups want to learn how to write better prompts and get more effective results from AI tools. A universal "AI Basics" module would serve all populations.
AI-assisted writing is a shared priority — whether for emails (Staff), academic papers (Students), or course materials (Faculty). A tiered writing module by role type is recommended.
Both Students and Faculty prioritize AI for research acceleration. A joint session on AI-powered literature discovery and synthesis would serve both groups efficiently.
Both Staff and Faculty want training on AI-driven data analysis. Staff focus on operational data; Faculty on research datasets — but core AI tooling is the same.
Staff and Faculty both want to use AI for automating repetitive processes — administrative for staff, grading/feedback for faculty. Shared sessions with role-specific examples.
Unique to IT Staff — evaluating enterprise AI platforms, managing security and compliance, and supporting institutional AI rollout. Requires a dedicated IT-focused track.
Unique to Faculty — setting course-level AI policies, detecting AI-generated content, and communicating expectations to students. Faculty-only governance training recommended.
Unique to Students — learning how to present AI competency in healthcare and research job markets, and leveraging AI for resume building and interview prep.