(The formatted use case description, include the three parts and the Initial Contribution, is available at: https://drive.google.com/drive/folders/11Qpv0zz_sutYagR6SXQ37PzDcRlLJ35r?usp=sharing )
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+1. What is the situation or context you are addressing?
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+AI has become a daily tool for the vast majority of university students. Most use general-purpose chatbots like ChatGPT to get answers to course-related questions, generate content for assignments, or prepare for exams. At the same time, a growing market of purpose-built "educational AI" platforms has emerged, typically offering a chatbot interface over course materials or teacher-created content.
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+This creates a paradox: AI is everywhere in education, yet its actual effect on learning is rarely questioned. Students have access to more assistance than ever, but the nature of that assistance (instant, frictionless answers) bypasses the cognitive effort that learning requires. Meanwhile, professors have lost visibility into how their students actually think, and existing tools give them no way to recover it.
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+This situation is particularly critical in large university settings, where professors cannot individually track students’ understanding, making this loss of visibility even more significant.
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+2. What is your critical analysis of this situation?
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+The dominant model for AI in education is content-centric: the AI retrieves or generates information, and the student consumes it. This is efficient, but it is pedagogically backwards. Research consistently shows that the effortful part of learning (confusion, trial and error, active reconstruction of understanding) is precisely what produces durable knowledge [5-9]. A system optimized to remove that friction is, in effect, optimized to impede learning.
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+This also creates a behavioral dependency: students progressively rely on AI to bypass difficulty rather than engage with it, which fundamentally alters how they approach learning tasks.
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+A tempting response would be to restrict what the AI can do: block answers, force students to ask differently, limit capabilities. We considered this approach and rejected it. A system that withholds help simply pushes students back to mainstream models, which are more capable and always available. Restricting the AI solves nothing; it just removes the student from a context where their learning can be tracked and supported.
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+3. What perspectives were discussed and how were they debated within your team?
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+The central tension we debated was between guidance and autonomy: how much should the system steer the student, and at what point does steering become replacing?
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+The position we converged on is that the companion should engage the student in thinking before delivering a response, not by withholding, but by asking. Where are you stuck? What have you tried? What do you already understand about this? This keeps the interaction genuinely helpful while creating a moment of reflection that a direct query to ChatGPT never would. The answer still comes; the difference is what happens before it.
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+We also debated the scope of the learning profile: how detailed should it be, and who controls it? We agreed that a profile the student cannot see, edit, or export is a liability both ethically and in terms of trust. GDPR compliance is therefore not a constraint we work around but a design principle we want to build from.
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+Another perspective we considered was whether forcing interaction constraints (e.g. delaying answers or enforcing step-by-step reasoning) could improve learning outcomes, but we concluded that excessive constraints would likely reduce adoption.
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+4. What contribution are you proposing, and under what conditions could it be implemented?
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+We propose a thinking-first AI infrastructure for higher education, built around three interconnected layers. This proposal outlines a conceptual framework that we intend to explore and refine during the project, rather than a fully specified or already implemented system.
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+The first is a learning profile layer. The system builds and maintains an individual profile for each student, capturing not just what they know but how they learn: their reasoning style, recurring difficulties, preferred exercise formats, and progress over time. This profile shapes every interaction (how the AI frames its questions, what exercises it generates, and how it adapts its responses) without replacing the student's effort.
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+The second is an exercise generation layer. Rather than simple content retrieval, exercises are generated by combining course material with the student's profile. The system selects format, difficulty, and focus based on where the student actually is, not where the syllabus assumes they should be. Students can also specify what kind of practice they want, and the system will adapt accordingly.
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+The third is a sovereign infrastructure layer. The entire system has to run on locally-hosted, open-source models through ILaaS, keeping universities in control of their data. Students own their cognitive profile: they can view it, edit it, and carry it from one course to the next. Teachers receive anonymized analytics (which concepts generate the most difficulty ,...) without access to individual student data.
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+For example, a student preparing for an exam in mathematics could interact with the system by attempting exercises. Instead of immediately providing the solution, the AI would first prompt the student to explain their reasoning, identify where they are stuck, and then adapt its guidance accordingly. Over time, the system would detect recurring difficulties and adjust the exercises to target those specific gaps.
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+For implementation, a proof-of-concept is feasible within our university environment using existing ILaaS infrastructure and open-source model tooling. The core technical challenge and main source of novelty lies in the learning profile: how it is defined, updated, and used to guide the system’s interactions with students. Our objective is to enable a coherent and strategic form of interaction between the AI and the student, grounded in scientifically established teaching patterns. This is the open question we intend to investigate most seriously, as it is what separates this system from a well-configured chatbot.
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+We also aim to evaluate the effectiveness of this approach by measuring student engagement, progression over time, and their ability to solve problems independently after interacting with the system.
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+[1] Freeman, J. Student Generative AI Survey 2025, Higher Education Policy Institute, Policy Note 61, February 2025
+[2] Burley, J. AI Agents in Higher Education, Institute for Ethics and Emerging Technologies, ISBN 979-8-9879599-4-7
+[3] Stadler, M. Bannert, M. Sailer, M., Cognitive ease at a cost: LLMs reduce mental effort but compromise depth in student scientific inquiry, Computers in Human Behavior, Volume 160, 2024, ISSN 0747-5632, https://doi.org/10.1016/j.chb.2024.108386.
+[4] Alshanetsky, E. When AI Dissolves Trust: Education Can Pioneer New Infrastructure. Soc 62, 982–987 (2025). https://doi.org/10.1007/s12115-025-01149-x
+[5] Roediger, H. L., & Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17(3), 249–255. https://doi.org/10.1111/j.1467-9280.2006.01693.x
+[6] Karpicke, J. D., & Roediger, H. L. (2008). The critical importance of retrieval for learning. Science, 319(5865), 966–968. https://doi.org/10.1126/science.1152408
+[7] Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students' learning with effective learning techniques. Psychological Science in the Public Interest, 14(1), 4–58. https://doi.org/10.1177/1529100612453266
+[8] Bjork, E. L., & Bjork, R. A. (2011). Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning. In M. A. Gernsbacher et al. (Eds.), Psychology and the Real World (pp. 56–64). Worth Publishers.
+[9] Stadler, M., Bannert, M., & Sailer, M. (2024). Cognitive ease at a cost: LLMs reduce mental effort but compromise depth in student scientific inquiry. Computers in Human Behavior, 160, 108386. https://doi.org/10.1016/j.chb.2024.108386
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