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Using AI with Cognitive Apprenticeship Theory, Upscaling and Retooling

Generative AI can be a useful tool in simulating real-word situations for learners. However, its successful implementation in higher education requires funds, data security, training and maintenance.

The cognitive apprenticeship model (Collins et al., 1989; Brown et al., 1991) can be a highly effective approach for designing online or hybrid professional and Continuing Education programs for adult learners (Ghefaili, 2003). It recognizes that expertise acquisition requires active engagement, practice and reflection and provides a framework for modelling, coaching, scaffolding and fading to guide student learning. By leveraging generative AI in digital learning, educators can help make learners’ thinking visible during innovative upskilling and retooling programs. Integrating generative AI into instructional design should help bridge the gap between industry and education, ensuring learners have the knowledge, skills and competencies they need to succeed in today’s rapidly changing economy.

Authentic and Adaptive Learning

The cognitive apprenticeship model emphasizes providing authentic learning experiences that allow learners to engage in complex real-world activities. Generative AI can be used to create realistic simulations and scenarios that provide learners with opportunities to apply their knowledge and skills in context. Earn/learn partnerships can utilize onsite chatbots and intelligent tutoring systems, creating learning environments that provide learners with psychological safety to practice, vocalize and refine their problem-solving skills.

The model also emphasizes modelling, coaching, scaffolding and fading to guide student learning. Generative AI can be used to provide adaptive feedback and support that helps learners develop their skills and knowledge gradually. AI can create visualizations of learners’ thinking processes using concept maps or decision trees, which can help learners understand their own thinking and facilitate discussions in team-based learning. Instructors can also use these visualizations to identify patterns in learners’ thinking and to provide targeted feedback and support for skills acquisition.

Additionally, generative AI can provide prompts and questions to scaffold personalized training, helping learners focus on key concepts and connect them to their prior knowledge and experiences. By making their thought processes explicit, learners can also receive instructor and peer feedback, leading to a more collaborative and supportive learning environment. Thus, integrating generative AI in cognitive apprenticeship can enhance the learning experience by providing personalized feedback and support, facilitating reflection and articulation, and promoting the development of metacognitive skills.

Reflection and Articulation

Cognitive apprenticeship emphasizes the importance of reflection and articulation to enable learners to make their tacit knowledge explicit. Generative AI can be used to support learners in reflecting on their learning and articulating their thought processes, which can help them develop essential metacognitive skills and a deeper understanding of underlying concepts. Research has shown that metacognitive skills can improve adult learner efficacy and lead to better performance in the workplace (Cheng & Chen, 2015). Cheng and Chen’s study on the impact of e-learning on on-the-job training found that e-learning interventions that provided opportunities for reflection and articulation enhanced adult learners’ metacognitive skills and facilitated transfer of learning to the workplace.

Obstacles to Implementing Generative AI for Cognitive Apprenticeships

Educators considering cognitive apprenticeships with generative AI to support upskilling and retooling should be aware of the potential challenges related to cost, privacy and technical expertise. The costs of implementing generative AI systems can vary depending on factors such as the complexity of the system and the amount of data required for training. Professional and Continuing Education leaders may need new partnerships to help cover upfront costs of hardware, software and training, as well as ongoing maintenance and updates.

Privacy concerns are also an important consideration when implementing generative AI systems for adult learners’ use in real time. Collecting, displaying and analyzing large amounts of data may raise privacy concerns, particularly if the data include personally identifiable information. Leaders and educators should take steps to ensure data is collected, stored and analyzed in compliance with relevant privacy regulations and that appropriate security measures are in place to protect against data breaches.

Finally, professional and Continuing Education units may face challenges in finding learning engineering talent with the specialized technical skills needed to develop and maintain generative AI systems. These skills may include expertise in machine learning algorithms, data science and software engineering. Professional education units may need to invest in training or hiring individuals with these skills or work with external partners to develop and maintain their systems.

In conclusion, the use of generative AI in skills-based learning has the potential to make learners’ thinking more apparent to themselves, group members and instructors. By creating learning environments that simulate authentic work situations and providing adaptive feedback and support, generative AI can help learners develop complex problem-solving skills and prepare them for success in today’s rapidly changing digital economy. However, education leaders should be aware of the potential obstacles that come with implementing generative AI for upskilling and retooling and plan accordingly to overcome these challenges.


Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18 (1), 32-42.

Cheng, W.-T., & Chen, C.-C. (2015). The impact of e-learning on workplace on-the-job training. International Journal of e-Education, e-Business, e-Management and e-Learning, 5(2), 107-111. Retrieved from

Collins, A., Brown, J. S., & Hoium, A. (1991). Cognitive apprenticeship: Making thinking visible. American Educator: The Professional Journal of the American Federation of Teachers, 15(3), 6-11, 38-46.

Ghefaili, A. (2003). Cognitive apprenticeship, technology, and the contextualization of learning environments. Journal of Educational Computing Design and Online Learning, 4(Fall), 1-27. Retrieved from

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