Generative AI is reshaping contemporary business practice, yet institutional responses in higher education remain uneven, often focusing on compliance or basic tool use rather than the higher-order capabilities required for responsible AI use (Rapanta et al., 2025). This creates a need to strengthen academic integrity through capability-building, particularly in ethical reasoning, critical evaluation, and professional responsibility.

This research co-designs, embeds, and evaluates AI literacy micro-learning modules to support integrity-resilient learning, with students as partners alongside educators and learning designers. It foregrounds the development of students’ epistemic vigilance, bias awareness, and responsible AI use (Nimri & Yang, 2024), supporting their ability to critically evaluate AI-generated content and make informed decisions about its appropriate use in academic contexts.

Adopting a co-design approach, the research progresses through five stages: Empathise (student and educator focus groups), Define (capability targets and success criteria), Ideate (co-designed solutions), Prototype (iterative development), and Test (embedding and evaluation across courses), informed by learning experience design principles (Boller & Fletcher, 2020).

Findings indicate that co-designed, embedded micro-learning enhances students’ confidence, engagement, and AI literacy capability. Preliminary evidence suggests improvements in students’ capacity to recognise bias, critically assess AI outputs, and engage more responsibly with AI-enabled learning tasks. The approach highlights the value of scalable, judgement-first learning design in supporting academic integrity in AI-enabled environments and demonstrates the benefits of positioning students as co-creators rather than passive recipients of AI literacy training, with potential application across disciplines and institutions.