Research
Extend inquiry without outsourcing judgment.
Use generative AI to explore literature, code, data, hypotheses, and communication while preserving traceability, methodological care, and human accountability.
Learn · research · prepare · share
AI for work that matters.
A practical starting place for BYU faculty and students learning how generative AI can support research, workplace preparation, and deeper learning.
The guidance is organized around capabilities, judgment, and repeatable practice—not a particular commercial platform.
Agentic workflow · human in the loop
- define the outcome, evidence, and limits
- provide approved context and tools
- inspect intermediate work and sources
- verify before using or sharing
Platform · open choiceReview · required
Choose a starting point
- Teach and learnFind workshops and a short course being developed for thoughtful, discipline-specific use.
- Work with agentsMove from chat to bounded workflows with checkpoints, evidence, privacy, and human review.
- Study the fieldCompare BYU’s Data Science BS and Machine Learning BS, then confirm current requirements in the catalog.
- Find collaboratorsBrowse a public-source map of 55 BYU faculty members, collaborations, and research groups.
- Analyze human movementSee how computer vision, biomechanics, and coaching judgment can work together in athletics.
Useful knowledge should travel.
BYU’s “share and lift” vision gives this initiative a clear test: help people learn sound methods, apply them responsibly, and pass what works to others.
Workplace
Practice the work, not just the interface.
Learn to define outcomes, decompose tasks, evaluate evidence, and communicate decisions across changing tools.
Learning
Make thinking more visible.
Use AI to prompt explanation, comparison, revision, and reflection—not to bypass the intellectual work education is meant to develop.
Programs in development.
Short, practical formats are being assembled around real faculty and student workflows. Dates and registration details will be added as they are confirmed.
Faculty workshop series
Hands-on sessions for research, teaching, course design, assessment, and responsible adoption. Each workshop centers a bounded task and a reusable method.
In developmentGenerative AI short course
A compact sequence covering foundations, prompting, retrieval, agentic workflows, evaluation, privacy, attribution, and discipline-specific practice.
In developmentData-driven coaching course
A developing athletics pathway that connects video quality, pose estimation, biomechanics, interpretation, privacy, and coach-facing communication.
In developmentBegin with the workflow, not the brand.
An agentic tool can plan steps, use approved tools, inspect results, and continue toward a goal. The durable skill is designing the boundaries around that work.
- 1 · Scope
- Name the outcome, permitted actions, protected information, stopping conditions, and the person responsible for final review.
- 2 · Context
- Provide only the files, data, policies, examples, and tools the task actually needs. Remove sensitive or unapproved material.
- 3 · Checkpoints
- Require a plan before action, source links for factual claims, visible intermediate outputs, and approval before consequential changes.
- 4 · Evaluation
- Test against representative cases. Check accuracy, bias, security, reproducibility, and failure behavior—not just a polished demonstration.
- 5 · Record
- Keep the prompt or task description, relevant inputs, tool actions, versions, outputs, and human decisions needed to understand the result.
Open courses for building the foundation.
APMonitor course materials provide a strong bridge from computing fundamentals to data science, machine learning, optimization, and dynamic systems.
Machine learning and data
- Machine Learning course ↗ — supervised and unsupervised models with applied projects.
- Data Science course ↗ — Python, data preparation, visualization, modeling, and applied projects.
- Data Engineering course ↗ — data pipelines, databases, cloud tools, and automation.