SYNAPSE: A Multi-LLM Orchestrated AI Tutor for Secure Software Development Education with Neurodivergent-First Design

2026-07-16

AI Summary

This study addresses the persistent lag of secure programming education behind foundational coding instruction and its inadequate support for neurodiverse learners, such as those with ADHD. To bridge this gap, the authors propose an adaptive AI tutoring platform designed explicitly for neurodiverse individuals, introducing a “neurodiversity-first” pedagogical framework. The system integrates 18 consistently available accessibility features and leverages the Model Context Protocol to orchestrate Claude, GPT-4o, and Gemini large language models. Guided by a Socratic three-stage prompting strategy, the platform facilitates a “detect–understand–remediate” learning cycle within the ShopSecure vulnerability environment. Pilot evaluation demonstrates a System Usability Scale score of 76.4, user engagement of 4.2/5, and comparable cognitive load between neurodiverse and neurotypical learners, affirming the platform’s innovative contributions to accessibility, personalization, and instructional efficacy.

Abstract

Developers who maintain real systems must continually recognise and remediate vulnerabilities in existing code, yet this skill is rarely trained directly: secure software development is commonly taught only after programming fluency is acquired, and accessibility support is treated as a secondary concern, disadvantaging learners with ADHD and related executive-function differences. This paper presents SYNAPSE, a publicly deployed adaptive tutoring platform for Java, Python for cybersecurity, and secure software development. SYNAPSE coordinates Claude, GPT-4o, and Gemini through the Model Context Protocol, routing interactions by pedagogical intent under a three-stage Socratic hint policy. It exposes eighteen always-visible accessibility features and anchors practice in ShopSecure, a deliberately vulnerable web application mapped to six OWASP Top 10 (2021) categories, on which learners practise the detect-understand-remediate loop characteristic of software maintenance. A feasibility pilot with nineteen participants across neurodivergent and neurotypical cohorts returned a System Usability Scale score of 76.4 and engagement of 4.2/5, with comparable cognitive-load levels across cohorts. SYNAPSE is available at https://synapse-course.com; a screencast is available at https://youtu.be/9R17KC47qQI.

Problem

• secure software development education
• neurodivergent learners
• vulnerability remediation
• accessibility in education
• executive-function differences

Innovation

• Multi-LLM orchestration
• Neurodivergent-first design
• Socratic hint policy
• Secure software development education
• Model Context Protocol