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Changes at "Establishing a sovereign infrastructure for higher education built on metacognitive student learning profiles."

Avatar: clement clement

Title (English)

  • +Establishing a sovereign infrastructure for higher education built on metacognitive student learning profiles.

Title (Français)

Body (English)

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    Team name: UTC EduTech

    Use of AI tools :

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      Open Source LLMs families: Gemma, Llama, Mistral

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      • LLMs are used within our application to answer student questions, either as user-facing LLM, or as integrated subagents.

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      We also used private LLMs to support the development of the application (coding assistants as Claude Code). All outputs were reviewed.

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    External feedback & contributions : Name – Role – Type of contribution (feedback, comments, mentoring, discussion, keynotes, etc.)

    People who supported UTC EduTech during the AI Grand Challenge 2026 process:

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    • Matthieu Bricogne-Cuignières - Associate Professor at Université de Technologie de Compiègne - Guided us from the start, presented us the UTC AI servers and how to use them, proposed resources to guide our reasoning.

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    • Stéphane Poinsart - Instructional Support Specialist, Content Engineering Unit (ICS) at Université de Technologie de Compiègne - Guided us on examples and existing AI platforms.

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    • Alain Goudey - Associate Dean for Digital, NEOMA Business School - Keynote presentation on AI usage, European skepticism, and legitimacy of generative AI in higher education

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    • Alexandra Pregent - CEA Saclay - Keynote on AI ethics, ALTAI framework for trustworthy AI, and harm foreseeability

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    • Bastien Guerry - Former developer at DINUM, open source maintainer - Keynote on open source licensing, AI sovereignty, and legal status of AI weights

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    • Caroline Beslin - Project methodology expert, Ecole Centrale de Lyon - Keynote on four-dimension project consolidation framework (Intellectual, Material, Social, Emotional)

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    • Justine Cassell - Senior researcher at Inria, on-leave Dean's Professor at Carnegie Mellon University - Community comment recommending examination of CMU's Open Learning Initiative (OLI)

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    • Mahdi Ayadi - Fellow AI Grand Challenge participant - Community comment on student behaviour and the need for structural rather than voluntary solutions

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    • Chinmay Das - Fellow AI Grand Challenge participant - Community comment on learning profile design and avoiding reinforcement of incorrect assumptions

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      Nipun Ranchhod Navadia - Fellow AI Grand Challenge participant - Community comment affirming the sovereign, student-owned learning profile concept and GDPR framing

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    Initial contribution: gid://decidim-app/Decidim::Proposals::Proposal/91

    Final contribution:

    Paper: https://drive.google.com/file/d/1dgdzOr-DXTSeDvOI3nMEaWISuAxj4-2J/view

    Website: https://alexandre.amrani.fr/utc_edutech_ai_challenge/

    Reflection on the process (important)

    We explain here how our contribution evolved during Phase 2:

    Comparison between initial publication (13 April 2026) and final main.tex

    --- Summary of Evolution ---

    The initial submission was a conceptual proposal describing an intended system.
    The final submission is a complete academic paper documenting a working minimum
    viable product with grounded theoretical work, explicit engagement with
    expert input, and transparent documentation of internal debates.

    The transformation occurred across five dimensions: specificity, theoretical
    grounding, debate transparency, community integration, and infrastructure depth.

    --- 1. Key feedback that influenced the work ---

    A) Keynote presentations (directly integrated into the argument structure)

    - Alain Goudey's keynote introduced the concept of "European specification" for
      AI (privacy-respecting, sustainable, cognitively sovereign). This reframed
      what was previously a technical constraint (using open-source models) into a
      deliberate design philosophy aligned with European values. The final paper
      dedicates substantial argument to this framing, which was absent from the
      initial submission.

    - Alexandra Pregent's keynote on the ALTAI framework and harm foreseeability
      gave us a structured vocabulary for ethical design. Her distinction
      between prevention and post-assessment directly shaped the final paper's
      discussion of paternalism and the graduated response structure (default,
      redirection, scaffolding modes).

    - Bastien Guerry's analysis of open source licensing and AI weights provided
      the legal-philosophical foundation for the sovereignty argument. The initial
      submission mentioned ILaaS as a technical option; the final paper positions
      sovereignty as inseparable from pedagogy, citing Guerry's argument that a
      model that is merely accessible rather than genuinely open does not confer
      sovereignty.

    - Caroline Beslin's four-dimension framework (Intellectual, Material, Social,
      Emotional) revealed that the team was over-investing in the technical
      dimensions and neglecting the social/emotional ones. This directly led to
      features like the transparent, editable profile dashboard that makes the
      student feel seen rather than surveilled.

    B) Community comments (explicitly addressed in Section III)

    - Mahdi Ayadi (29 April) argued that students knowingly act counterproductively
      and that a "better tool" won't fix a behavioural problem. This pushed the
      team to make the thinking-first interaction the default, non-toggleable
      pattern rather than an optional mode. The final paper acknowledges this
      explicitly and structures the response around structural rather than
      voluntarist solutions.

    - Chinmay Das (21 April) asked how the learning profile would avoid reinforcing
      incorrect assumptions. This led to the hybrid model where AI-generated
      inferences are presented transparently to the student, who can annotate and
      correct them. The final paper dedicates a full paragraph to this design
      choice and cites Das by name.

    - Justine Cassell (30 April) noted that learning profiles already underlie
      successful AI tutors and recommended studying Carnegie Mellon's Open
      Learning Initiative (OLI). This challenge strengthened the proposal by
      forcing the team to articulate what was already there and how they
      differ from it. The final paper
      explicitly states the differentiation: OLI does not build persistent
      profiles that travel with the student across courses and institutions,
      nor does it give students control over their data.

    - Nipun Ranchhod Navadia (10 May) affirmed that the sovereign, student-owned
      learning profile concept is "strong and timely" and highlighted the
      rebalancing of power between institution and learner. This validated the
      direction and is cited in the final paper's discussion of portability.

    --- 2. Specific changes made as a result ---

    A) From conceptual to implemented

      Initial: "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."

      Final: Describes a working system with FastAPI backend, React frontend,
      Supabase PostgreSQL, multi-agent orchestration, agentic RAG architecture,
      and three-state interaction classifier. The paper includes a dedicated
      Section V ("What Was Built") with full implementation details.

    B) From "thinking-first" to "metacognitive" framing

      Initial: Used "thinking-first" as the core concept, with a single interaction
      pattern of "ask before answer."

      Final: Adopts "metacognitive" as the precise learning-science term. The
      interaction model is now three distinct modes (default Socratic inquiry,
      redirection when seeking shortcuts, scaffolding when genuinely stuck) with
      state detection based on lexical patterns and conversation history.

    C) From vague profile to structured 7-dimension model

      Initial: Mentioned a "learning profile" capturing "reasoning style, recurring
      difficulties, preferred exercise formats, and progress over time."

      Final: Specifies exactly seven dimensions: dominant reasoning style, recurring
      misconceptions, demonstrated competencies, preferred exercise format, proximal
      zone indicators, engagement signals, and recent progress. Each is grounded in
      specific learning science frameworks (Bloom's taxonomy, Zimmerman's SRL model).

    D) Profile transparency became a feature, not just a principle

      Initial: "A profile the student cannot see, edit, or export is a liability."

      Final: Implements a dedicated dashboard where students read, annotate, and
      correct the system's inferences. Instructors only access anonymised aggregate
      data. This design directly addresses Chinmay Das's concern about silently
      reinforcing incorrect assumptions.

    E) Addition of a "direct answer" mode

      Initial: Framed the system as purely thinking-first, with no alternative.

      Final: Pragmatically adds an option to send messages to a direct chatbot
      with no Socratic discourse, acknowledging that students will otherwise go
      to ChatGPT anyway. This was a direct response to Ayadi's behavioural argument.

    F) Debates section was created from scratch

      Initial: Had one short paragraph about "the central tension we debated
      between guidance and autonomy."

      Final: Section III is a full argument documenting four major debates
      (equity vs friction, sovereignty vs capability, paternalism, profile
      control) plus the Cassell challenge. Each debate presents both positions,
      explains the arbitration, and cites the relevant input that shaped it.

    G) References expanded from 9 to 20+

      Initial: 9 references, all cognitive science papers.

      Final: 20+ references including 4 keynote speaker citations, 4 expert
      resource citations, 4 community comment citations, and additional
      academic sources on intelligent tutoring systems (VanLehn, Kulik),
      motivation theory (Deci & Ryan), self-regulated learning (Zimmerman),
      and pedagogical AI research (Favero, Puech).

    H) Infrastructure detail

      Initial: "Run on locally-hosted, open-source models through ILaaS."

      Final: Specifies Gemma and Mistral model families, institutional hardware
      at UTC, fallback client architecture with graceful degradation, and
      explicitly addresses the performance trade-off with frontier proprietary
      models, citing Favero et al. for independent validation.

    Video pitch

    https://drive.google.com/file/d/1zRzLU1gECv2wfYPfPy3K7zn2xVD0dNBv/view

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