A methodical approach to AI-supported human learning in complex task environments

Abstract

Hybrid intelligence aims to combine human and AI based on their complementary strengths and weaknesses, with the goal of reaching better results than either could achieve on its own, and to continuously improve (Dellermann et al., 2019). To research hybrid intelligence is the aim of the HORIZON project AI4REALNET (cf. ai4realnet.eu), which develops AI-based solutions addressing critical systems (electricity, railway and air traffic management) that are traditionally operated by humans, and where AI systems complement and augment human abilities. In the project continuous improvement of hybrid intelligence is considered in co-learning scenarios, which aim to enable humans and AI to learn from each other. The paper proposed in this abstract focuses on the human side of co-learning, i.e. on how human learning can be specifically and systematically supported in settings of hybrid intelligence. However, in human-AI collaborations in which the AI provides opaque recommendations for solving complex problems, human learning processes will rather not be systematically supported. Just as students cannot learn to solve complex math problems if the teacher simply reveals the solution to them. Rather the teacher needs to enable the students to understand the problem and to find ways to solve the problem. Essentially, within our framework of hybrid intelligence, AI must explicitly support human cognitive learning processes in relation to the problem and the problem-solving to systematically support human learning. Against this background, In the HORIZON project “AI4REALNET” the “Supportive AI Framework” was developed (Waefler et al., 2025), which aims at an intensified human-AI collaboration (Waefler, 2021). It conceptualizes human learning on the basis of the «Experiential Learning Theory” (Kolb & Kolb, 2009), which considers learning as a cycle of four phases: •Concrete experience: Making new experiences within the relevant domain of knowledge or transcending existing ones.•Reflective observation: Reflecting on experiences and considering what was successful or where there is room for improvement.•Abstract conceptualization: Conceptualizing thoughts, adapting existing ideas or developing new ones in order to abstract understanding enabling the construction of new mental models or conceptual frameworks.•Active experimentation: Testing the cognitive representations acquired in the previous phases and getting feedback from practice, based on active experimentation.The paper describes in detail how these phases of experiential learning can be specifically and systematically supported by AI so that human domain experts continuously improve their explicit and tacit competencies. ReferencesDellermann, D., Calma, A., Lipusch, N., Weber, T., Weigel, S., & Ebel, P.A. (2019). The Future of Human-AI Collaboration: A Taxonomy of Design Knowledge for Hybrid Intelligence Systems. https://doi.org/10.48550/arXiv.2105.03354.Kolb, A.Y. & Kolb, D.A. (2009). The learning way. Meta-cognitive aspects of experiential learning. Simulation & Gaming, 40(3), pp. 297-237.Waefler, T. (2021). Progressive Intensity of Human-Technology Teaming. Proceedings of the 5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021, August 27–29, 2021, France, pp. 28-36.Waefler, T., Hamouche, S., Eisenegger, A. (2025). The Supportive AI Framework: From Recommending to Supporting. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Augmented Cognition. HCII 2025. Lecture Notes in Computer Science(), vol 15778. Springer, Cham. https://doi.org/10.1007/978-3-031-93724-8_22

Keywords: Hybrid intelligence, Supportive AI, Co-learning, Experiential learning, Situation awareness

DOI: 10.54941/ahfe1007163