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A5. HITL AI Scaffolding — Autonomy-Supportive Design
Core finding: AI scaffolding must support autonomy, not replace executive function. Controlling or fully automated approaches undermine the self-regulation skills they aim to support. The distinction is not philosophical but empirical.
Self-Determination Theory (SDT) framework for ADHD:
- Champ, Adamou & Tolchard 2022 (Psychological Review): Proposed a complete SDT-based framework for ADHD, arguing that autonomy, competence, and relatedness needs explain self-regulation patterns better than deficit models.
- Champ et al. 2025 (JMIR Formative Research): ADAPT randomised feasibility study with 20 adults from an NHS ADHD clinic. 91.6% intervention completion. Clinically significant improvement in psychological distress (p = .01) and significant ADHD symptom reduction (p ≤ .01). Demonstrates that autonomy-supportive scaffolding works in clinical practice.
Critical review of existing ADHD tools:
- Spiel et al. 2022 (ACM CHI '22): Most ADHD technology is "shaped by research aims which privilege neuro-normative outcomes." Time-management interventions frequently cause stress and frustration. Participatory design with ADHD individuals leads to fundamentally different design outcomes (e.g. conceiving time as "stretches of activities" rather than clock-based units). Explicitly documents harm caused by surveillance-like monitoring and intrusive alarms.
- Carik et al. 2025 (ACM GROUP '25): LLM use across 61 neurodivergent Reddit communities, identifying 20 use cases. ADHD users primarily sought help with organisation, planning, and prioritising. LLM responses are frequently "overly neurotypical" and not calibrated for neurodivergent cognition. Users expressed significant concern about overreliance.
Longitudinal case evidence:
- Mittler 2025: 42-year-old neurodivergent student with severe executive dysfunction. Over 4 semesters using strategically integrated AI tools, GPA rose from 1.85 to 3.35. Psychological trajectory shifted from anxiety to sophisticated "process awareness" — the student internalised external scaffolds.
- Azevedo et al. 2022 (Frontiers in Psychology): Decade-long MetaTutor programme, 100+ college students. Adaptive pedagogical agents that prompt metacognitive strategies (rather than completing tasks) produced significantly better learning outcomes.
Five design principles from the literature:
- Scaffold, don't automate — prompt metacognitive strategies rather than completing tasks for the user
- Co-regulate, don't correct — nudges should be reflective ("What were you working on?") rather than directive ("You should be working on X")
- Adapt to fluctuating states — detect attention shifts and adjust support intensity dynamically
- Keep the human in the loop — every AI suggestion requires user confirmation, building executive function rather than atrophying it
- Design with, not for — participatory design with neurodivergent users produces fundamentally different and better outcomes
Implication for Kon: The AI agent must be visible, conversational, and interactive — but must never override user autonomy. Every suggestion requires confirmation. The human-in-the-loop feedback mechanism builds metacognitive awareness over time. Users should eventually internalise Kon's scaffolding patterns and need them less — that's a feature, not a failure. LLM prompts must be calibrated for neurodivergent cognition, not neurotypical assumptions.