From the Student Success edition of October 22, 2026
Inside Higher Ed opinion: APA and Springfield College learning scientists argue the science of learning should guide AI use, and call for small iterative studies testing transfer without AI
American Psychological Association; Springfield College (authors) · Higher education teaching and learning design for generative AI · United States
- Publisher
- Inside Higher Ed
- Original publication
- Inside Higher Ed (Views), 22 October 2026
- Source retrieved
- 2026-10-23
What happened
In a 22 October Inside Higher Ed opinion essay, Beth M. Schwartz (APA senior director of teaching and learning) and Christopher Hakala (Springfield College) accept that evidence on whether generative AI improves student learning remains limited and that some studies suggest unguided use could undermine it. They argue that the established science of learning still gives a framework: learning requires targeted cognitive effort, so the question is whether a specific AI use strengthens or replaces that effort. They propose that faculty start from a learning goal and ask which cognitive work must remain with the student and whether students can demonstrate learning independently. They recommend that institutions support smaller, iterative studies of specific AI uses, comparing student work before and after AI assistance, testing transfer without AI and comparing results across disciplines and student populations.
Why it matters
Gives student-success leaders an evaluation frame for AI pilots that centres independent transfer and subgroup differences rather than tool adoption, relevant given recent mixed course-level trial results.
Evidence and measured results
Expert opinion grounded in prior learning-science literature; no new data.
Limitations and uncertainty
Opinion, not research; the authors acknowledge the AI-specific evidence base is limited. Recommendations are not tested here.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-10-23; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Institutions are investing in AI for learning without a practical way to judge which uses help students learn.
- Stakeholders
- Teaching and learning centre
- Institutional research
- Faculty senate or curriculum committee
- Provost
- IRB or research office
- Discovery questions
- How are AI pilots evaluated today?
- Do you test whether students can perform without AI?
- Do you compare results by student group?
- Who funds small evaluation studies?
- Value hypothesis
- Interpretation: use-case-level evaluation could help institutions scale AI uses that support learning and stop those that replace it.
- Potential engagement
- A bounded evaluation design for one or two AI uses: learning goals, AI-free transfer measures, subgroup analysis and a one-term reporting cycle.
- Unsupported claims
- That any specific AI tool improves learning
- That learning-science principles alone guarantee results
- Effect sizes for AI uses
Pre-sales engineering
Role takeaway
- Fit
- Fits institutions running AI pilots in courses that can add evaluation instrumentation.
- Architecture and integration
- LMS and AI tool logging to capture drafts and interactions, plus AI-free assessment settings.
- Prerequisites
- Consent and research approval
- Draft and interaction capture
- AI-free assessment capability
- Access to subgroup data
- Constraints
- Opinion piece without tested methods
- Small studies may lack statistical power
- Privacy limits on interaction data
- Security
- Handle captured drafts and AI logs as student records under research and privacy review.
- Proof of value
- Pilot one before-and-after comparison
- Run an AI-free transfer task
- Check subgroup sample sizes
- Faculty review of design questions for one course
Delivery
Role takeaway
- Work
- Set up an iterative evaluation routine for course-level AI uses.
- Dependencies
- Faculty partners
- Institutional research support
- Research approval
- LMS data access
- Ownership
- Teaching and learning centre with institutional research.
- Skills and adoption
- Faculty development on mapping learning goals to permitted AI uses and designing transfer checks.
- Governance checkpoints
- Research and privacy approval
- Learning-goal mapping before pilot
- End-of-term results review
- Scale, revise or stop decision
- Acceptance criteria
- Proposed: each piloted AI use documents a learning goal and AI-free transfer measure
- Proposed: results reported by discipline and student group where sample allows
- Proposed: a scale, revise or stop decision recorded each term
- Risks
- Underpowered studies
- Faculty workload
- Selection bias in pilot courses
- Results ignored in procurement decisions
Implementation considerations
Lighthouse Advisory interpretation across the operating dimensions a public-sector buyer must settle before this evidence becomes a design. Each note answers the question under its heading for this specific source.
Architecture and integration
What must connect, and where does the AI sit in the workflow?
Limited technical relevance. Interpretation: evaluation designs that compare work before and after AI assistance require tools that can capture drafts and AI interactions with consent.
Governance
Who approves, reviews and stays accountable for outcomes?
Recommends institutions fund small iterative studies and judge AI uses against learning goals, shifting governance from tool approval to use-case evaluation.
Security and privacy
What data, permissions and controls need testing?
Not addressed. Interpretation: before-and-after work comparisons involve student data that needs research and privacy review.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Calls for comparing results across student populations and expertise levels, which supports equity analysis of AI uses.
Procurement
What should contracts, pricing and exit terms secure?
Purchase decisions could require vendors to support use-case-level evaluation and transfer testing.
Operating model
Which teams own the service once it runs?
Places design decisions with faculty and evaluation with teaching centres and institutional research.
Publication history
- 2026-10-22Student Success · Issue 494 resources
Stable resource ID: ihe-opinion-science-of-learning-guide-ai-use-iterative-studies-transfer-20261022