Scaffolding the Scaffold: Co-Evolving Human-Centered Boundaries for GenAI in Education

Related Publication: Scaffolding the Scaffold: Co-Evolving Human-Centered Boundaries for GenAI in Education

Full Paper

This blog post describes research presented at HHAI 2026. Co-authored with Yarne Dirkx, Davy Vanacken, and Gustavo Rovelo Ruiz.

What We Did

Educators today are largely stuck choosing between two extremes for GenAI: ban it, and risk losing real pedagogical opportunities, or allow it, and risk students leaning on it so much that foundational skills like writing, critical thinking, and problem-solving never fully develop. Surveys of top university guidelines show that the vast majority already push instructors toward course-specific GenAI rules, but most of these rules still address only surface-level concerns like plagiarism, and apply the same policy to every student in a course regardless of where they are in their learning journey.

That last part is the gap we focus on. Decades of research on scaffolding tell us that effective support should adapt as a student progresses, staying within their Zone of Proximal Development (ZPD): challenging enough to be useful, but not so far beyond their current ability that it becomes just doing the work for them. A one-size-fits-all GenAI policy can’t do that. A student who has already mastered a skill and a student who has never attempted it are held to the exact same boundary, even though research shows the same tool can help one and hurt the other.

So instead of asking “Can this student use GenAI?”, a binary question that ignores where the student actually is, we ask: what interactions with GenAI serve this student’s growth right now? This paper proposes a reconceptualization: adaptive boundaries, GenAI constraints that co-evolve with a student’s demonstrated competency, rather than static rules set once at the course level.

Our Framework: Adaptive Boundaries as Pedagogical Scaffolds

A Three-Party Negotiation

We frame adaptive boundaries as a human-AI collaborative negotiation between three parties:

1. Students, who signal competency through their learning behaviors — for example, the depth of questions they ask a GenAI system, patterns in how they complete tasks, or their ability to critically evaluate GenAI output.
2. Instructors, who define learning goals and pedagogical milestones — for example, a writing instructor might require students to demonstrate proper argumentation before GenAI-assisted writing is unlocked, while a programming instructor might allow code generation immediately but require students to explain and modify whatever it produces.
3. The GenAI system itself, which mediates between the two by adapting which of its own features are actually available to a given student at a given time.

Instructors set the milestones; the GenAI system then acts as an adaptive layer that scaffolds its own available features based on each student’s detected competency. Critically, this adaptation is bidirectional — boundaries loosen as competency grows, but they can also tighten again during assessments, or if a student’s interaction patterns start to suggest over-reliance rather than growth.

Three Developmental Stages

Drawing on established models of skill acquisition (the Dreyfus model) and mastery learning (Bloom), we describe three stages where GenAI plays a fundamentally different role:

• Novice stage — most restrictive. Early, unrestricted GenAI access risks undermining exactly the foundational skills a student is trying to build, and the same tool can help one student while hurting another, so the system first needs to build a model of the student before personalizing anything. At this stage, GenAI should expose its reasoning, require metacognitive reflection before answering, and only give direct solutions after a genuine attempt has been made.
• Intermediate stage — more flexible, but monitored. Boundaries loosen enough to target specific competency gaps, but the main risk shifts to over-reliance: students offloading cognitive work onto GenAI instead of building their own skills. The system should watch for signs like passive help-seeking without articulating the underlying knowledge gap, or uncritical acceptance of AI output, and temporarily tighten access if those patterns show up.
• Advanced stage — largely open. GenAI functions closer to a professional tool or collaborator. Even here, boundaries tighten again during assessments where students must demonstrate independent competency, and — importantly — boundaries should include aspirational scaffolds that push slightly beyond what’s already been demonstrated, so a competency-gated system doesn’t accidentally cap a student’s growth at what they’ve already shown.

Together, these stages turn what looks like an access-control policy into an actual pedagogical instrument that grows with the student.

Why This Is Feasible Now

We didn’t want this to be purely speculative, so a large part of the paper argues that the pieces needed to build adaptive boundaries already exist — just not combined yet.

On the pedagogical side, the ZPD and scaffolding theory already establish why support should shrink as competency grows; mastery learning already establishes competency-based (rather than time-based) progression; and Intelligent Tutoring Systems already prove that fine-grained, competency-based adaptation works at scale. Adaptive boundaries extend that logic in two ways: instead of adapting content inside a closed, instructor-authored system, they adapt a student’s access to an external tool used across tasks and courses, and instead of treating the tutoring system as the adaptive instrument, they treat governance itself as the thing that adapts.

On the technical side, learning analytics research has already shown that interaction data — clickstreams, process mining, language analysis of student-GenAI conversations, and classifiers trained on interaction patterns — can reliably surface competency signals. Meanwhile, mainstream GenAI platforms (ChatGPT, Claude, Ollama) already support the kind of personalized, configurable behavior adaptive boundaries need through system prompts, meaning a single GenAI system could plausibly implement all three stages via prompting rather than requiring bespoke infrastructure per stage.

So the missing piece isn’t a new capability — it’s the conceptual integration of governance and configuration with developmental scaffolding: boundaries that are student-specific, evidence-driven, and designed to evolve as competency changes.

Future Research Directions

We lay out four research questions that we think need to be answered before adaptive boundaries can move from concept to reality:

• RQ1 — Negotiation: How should boundaries be negotiated among students, teachers, and GenAI systems, balancing teacher authority, student agency, and equity vs. personalization?
• RQ2 — Detection: What interaction patterns reliably indicate a student is actually ready for a boundary shift, without oversimplifying (e.g., mistaking a short-term performance spike for real understanding)?
• RQ3 — Transparency: How can the logic behind a boundary change be made understandable to students, teachers, and administrators alike, without creating cognitive overload or over-explaining?
• RQ4 — Evaluation: How do we tell whether adaptive boundaries actually work — not just immediate task performance, but durable skill retention, appropriate reliance on GenAI, and student agency — evaluated against both no-GenAI and static-boundary baselines, not just each other?

These questions also surface real risks worth naming up front: competency signals may not work equally well across different student backgrounds, “readiness” is hard to standardize across disciplines, and a boundary that scaffolds one student might quietly cap another.

Conclusion

The core argument of this paper is that GenAI governance in education doesn’t have to be a binary policy decision applied the same way to everyone. The pedagogical theory (ZPD, scaffolding, mastery learning) and the technical capability (learning analytics, configurable GenAI) to do better already exist — what’s missing is putting them together into boundaries that are student-specific, evidence-driven, and built to evolve.

If we extend this work further, the most important next steps are the four research questions themselves: running participatory sessions to understand how negotiation should actually work, testing which interaction signals genuinely predict readiness, prototyping transparency mechanisms that scale from simple to in-depth explanations, and designing the mixed-methods, multi-baseline studies needed to evaluate whether any of this actually improves learning outcomes.

Ultimately, we want GenAI in education to stop being a fixed policy line and start being what scaffolding always was supposed to be: something that knows when to help, when to challenge, and when to step back.

This blog post is based on research presented at HHAI 2026. For complete details, see the full paper.