AI-assisted academic workflows
Practical workflows that use AI to accelerate reading, structuring and comparing academic material, with expert validation, clear controls and human decision-making built in.
AI accelerates the analysis. People make the decisions.
Used carefully, AI can take on the most repetitive parts of academic comparison work: reading long outlines, extracting outcomes and suggesting where they may correspond. Used carelessly, it produces confident-sounding output that no one has checked. Our workflows are designed for the first and to prevent the second.
Practical applications
Outcome extraction
Pulling learning outcomes, topics and assessment information from outlines into a consistent structure.
Candidate alignments
Suggesting possible outcome-to-outcome matches for reviewers to confirm, revise or reject.
High-volume first pass
Preparing structured comparisons for large batches of transfer-credit requests.
Evidence summaries
Drafting rationale and source citations for reviewer confirmation.
Consistency checks
Comparing new findings with past decisions to surface inconsistencies.
Documentation drafting
Producing first drafts of decision records and reports for human editing.
Who does what
Every workflow defines, in writing, which steps are AI-assisted and which require a qualified person.
| Task | AI assistance | Qualified reviewer |
|---|---|---|
| Reading source material | Extracts and structures outcomes | Checks completeness and currency |
| Comparing outcomes | Suggests candidate alignments | Confirms, revises or rejects each one |
| Ambiguous cases | Flags them for attention | Resolves them using disciplinary judgement |
| Documentation | Drafts summaries and records | Edits and approves the final record |
| Decision | None | Made by the institution's designated authority |
Risk controls
Controls are agreed with you and documented as part of each workflow.
- Human validation of every finding before it is reported or relied on
- Confidential material processed only in tools and environments your institution approves
- No use of institutional or learner data to train public AI models
- Inputs, prompts and tool versions recorded so results can be traced
- Sampling and spot checks against independent human comparison
- Known limitations, such as language, discipline or format, documented and monitored
- A clear fallback to a fully manual process at any stage
[Confirm the specific AI tools, hosting locations and data-handling terms you use or recommend, and update this list to match.]
Workflow design and implementation support
Assess
Identify where time is spent, what can be safely assisted, and the policy, privacy and security requirements that apply.
Design
Define the workflow, review points, quality checks and records, using platforms your institution already approves wherever possible.
Pilot
Run the workflow on a defined sample alongside the existing process and compare results.
Train
Guidance and practical training for the staff who will run and review the workflow.
Hand over
Documentation, templates and a review schedule so the workflow stays reliable over time.
Explore a controlled pilot
Start with a small, well-defined sample and decide on wider use with evidence in hand.