Teaching AI literacy as professional competency: a scaffolded framework across two health and social care modules

Generative AI presents health and social care programmes with a dual challenge: students must learn to use these tools responsibly in academic work while developing the critical evaluation skills needed for professional practice where AI is increasingly present. This lightning talk presents a scaffolded approach to AI literacy developed across two modules at different SCQF levels, designed for blended cohorts including international students.

At Level 7/8, a Traffic Light Framework provides clear behavioural boundaries — green (appropriate uses such as brainstorming and grammar support), amber (use with caution, such as research starting points requiring verification), and red (prohibited actions including submitting AI-generated text as original work). The framework is anchored in UWS Academic Integrity policy and introduced before students access AI tools.

At Level 9, students progress from following boundaries to evaluating AI critically, applying the NASSS complexity framework and NICE Evidence Standards to assess AI technologies in healthcare, examining bias, digital exclusion, and professional accountability when AI contradicts clinical judgment.

Two students were flagged for potential AI misconduct during formative assessment, handled through educational follow-up rather than punitive escalation, reinforcing the framework's developmental intent.

Key Takeaways
A replicable Traffic Light Framework (green/amber/red) for responsible AI use that can be adapted to any discipline.

A scaffolding model for progressing students from behavioural compliance at lower levels to critical professional evaluation of AI at higher levels.

An educational rather than punitive approach to AI misconduct that reinforces developmental intent.