Guiding "AI-Powered Learning": A Scaffolded Approach to Organisational Trust

While much attention is given to learning about AI, the greater lever for sustainable performance is using AI for learning. Yet, current benchmarking suggests AI integration in skills development remains low (Fornasiero et al., 2025). This tallies with what I see as an independent practitioner. To really help colleagues optimise learning with AI, organisations must look not just at the “AI plumbing” like data, cloud capability, and infrastructure, but also at their cultural and psychological readiness for learning with AI.

Adopting AI for learning can introduce acute tech anxiety. If we launch interventions too far ahead of a learner's current capability, it can just bounce off, or individuals can feel overwhelmed and disempowered. In learning psychology, Vygotsky (1978) emphasised the importance of meeting learners exactly where they are. Since we are all different, that means tailored approaches, calibrated to organisation and individual readiness levels.

To bridge readiness gaps and build trust, organisations should use Wood, Bruner, and Ross’s (1976) concept of "scaffolding." Instead of confronting an anxious learner with a blank prompt box and expecting high-level synthesis, we must provide structural safety nets. In practice, this means tailoring support by offering pre-built prompt templates for skill-building, or sharing use cases from relevant colleagues around how AI helped with their learning. Scaffolding reduces the cognitive load, allowing trust in the technology to grow incrementally.

Getting the language right is also important, and peers can help here too; the way they describe learning with AI can be easier to understand than your AI specialist, as Vygotsky’s theory of social learning would predict. So when a colleague explains in everyday language how they used AI to coach them through a new process, it demystifies the tool far better than an expert-led IT lecture on learning with AI.

By scaffolding the experience and fostering horizontal peer learning, organisations can move employees safely along the AI learning maturity curve—avoiding forced compliance, and instead promoting a culture rooted in trust and realistic goals.

Institute's Reflection:

This perspective highlights an important distinction: learning about AI is not the same as learning with AI. Organizations may provide access to tools and explain their capabilities, yet employees can still feel uncertain about how to use them for their own development. The challenge is therefore to create learning experiences that meet people where they are and gradually strengthen their confidence and capability.

The contributor's emphasis on readiness is particularly important. Research suggests that people's responses to AI can shape how they engage with it at work. Employees who experience AI as supporting their autonomy are more likely to actively adapt their work, while AI anxiety can lead to avoidance (Liu et al., 2025). This suggests that simply introducing more advanced tools or expecting immediate experimentation may not produce meaningful adoption. The same intervention can feel enabling to one person and overwhelming to another.

The contributor's suggestion of using pre-built prompt templates provides a practical example of how scaffolding can support learning with AI. Recent experimental research found that structured prompting can help people engage more deeply with problems and strengthen critical reasoning compared with simply asking AI for answers (Gerlich, 2025). Prompt templates may therefore do more than make AI easier to use: when designed well, they can guide people toward more thoughtful ways of interacting with AI while providing the structure and confidence needed to begin.

Our own Sense-Making Labs reached a similar conclusion. Participants repeatedly emphasized the value of spaces where people can share how they use AI, compare practices, experiment, and learn from one another. This suggests that organizations may need to move beyond one-off AI training toward a more developmental approach: providing different levels of support, creating opportunities for guided practice, and making peer learning part of everyday work.

References:

• Fornasiero, R., Kiebler, L., Falsafi, M., & Sardesai, S. (2025). Proposing a maturity model for assessing Artificial Intelligence and Big data in the process industry. International Journal of Production Research, 63(4), 1235-1255.

• Gerlich, M. (2025). From offloading to engagement: An experimental study on structured prompting and critical reasoning with generative AI. Data, 10(11), Article 172.

• Liu, Q., et al. (2025). How does organizational AI adoption affect employees' job crafting? The mediating roles of AI-supported autonomy and AI anxiety. Frontiers in Psychology.

• Vygotski, L. S. (1978/1979). Mind in society. The development of higher psychological processes. Cambridge, MA: Cambridge University Press

• Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of child psychology and psychiatry, 17(2), 89-100.