What support should I ask my institution for before helping introduce AI-supported learning? For a medical laboratory science educator considering that question, “more training” may be only part of the request. You may also want clarity about the teaching purpose, your part in decisions and how the proposal will be assessed. Start with four headings: purpose, preparation, participation and review.
Preparation involves more than training
A 2026 US study of medical laboratory science education offers a reason to broaden the support conversation. In a voluntary survey of 67 program directors, respondents reported constraints involving curriculum time, faculty expertise, budgets and infrastructure. These barriers concerned the broader set of technologies studied, not AI alone. [1]
Directors emphasized expanded faculty training, stronger institutional support and standardized national curriculum guidelines. Their responses put educator preparation alongside questions of time and resources. For someone preparing a support request, the useful distinction is between asking for a course and asking what would make preparation possible. [1]
The researchers also reviewed 489 accredited US program websites, finding little visible AI or bioinformatics content. That describes public-facing information, not a complete inventory of what educators teach. The survey findings are more directly useful here: they identify reported constraints worth examining, rather than establishing what any particular institution needs. [1]
Preparation is a shared planning question, not simply a request for individual enthusiasm.
Participation belongs in the discussion
A separate 2026 study offers another perspective on educator support. Through two modified Delphi consultation rounds, 15 interdisciplinary experts developed a proposed human–AI collaborative teaching framework for undergraduate physiology laboratory education. Their priorities included teacher leadership, targeted training, ethical safeguards and phased implementation. [2]
This is indirect evidence for medical laboratory science educators because the educational setting differs. It also answers a different question from the US survey: the directors reported constraints, while the physiology panel agreed on proposed priorities. The panel’s endorsement makes educator participation a useful subject for discussion; it does not demonstrate that its framework improves learning. [2] [1]
Four headings for your support conversation
In practice — editorial guidance
Use purpose, preparation, participation and review to organize a short discussion note. This is an editorial planning framework, not a tested implementation checklist. You do not need a complete technology proposal to begin asking clear questions.
- Purpose: Describe the teaching need before discussing a tool. For example, you might ask, “What learning need are we trying to address, and why are we considering AI rather than another approach?” Leave room for a decision not to proceed.
- Preparation: Separate learning needs from the resources needed to meet them. For example, you might ask, “What would faculty need to learn, and what preparation time and support could be made available?” Avoid treating attendance at one session as the whole preparation plan.
- Participation: Ask how educators will contribute to teaching decisions and raise concerns. For example, you might ask, “Who will discuss the educational choices and ethical questions, and how can faculty help shape the proposal?” Seek clarity about responsibilities, not simply expressions of enthusiasm.
- Review: Discuss evaluation before committing to a direction. For example, you might ask, “What evidence would we examine, when would we review it, and what would lead us to continue, revise or stop?” Keep the intended learning purpose visible throughout that conversation.
Bring the questions that matter most to your role, rather than an exhaustive wish list. A useful first meeting can end with unresolved issues clearly named and a next discussion agreed. Preparation is a shared planning question, not simply a request for individual enthusiasm.
What the research can and can't tell us
The voluntary US survey may be affected by self-selection, and its representativeness cannot be established from the available evidence. Reported barriers do not establish causation. Website descriptions may omit teaching content, so low visibility cannot establish curriculum absence. [1]
The Delphi findings reflect agreement within one expert panel, not demonstrated feasibility across institutions. Neither study establishes that training, institutional support or phased adoption improves learning, reduces workload or produces equitable outcomes. Neither supplies Philippine evidence. For Filipino educators, these studies offer questions to consider—not a description of local conditions or a reason to assume AI adoption is necessary. [2] [1]
References
- Muslu AM, Thompson C, Williams J, et al.. Advanced molecular detection, bioinformatics, and artificial intelligence era in medical laboratory science education. (2026).
- Wu H, Liu Y, Cui M, et al.. Designing a human-AI collaborative model for precision teaching in undergraduate physiology laboratory education: a modified Delphi study. (2026).
