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

• URL: http://arxiv.org/abs/2607.14601v1
• Date: Thu, 16 Jul 2026 06:01:55 GMT
• Title: SYNAPSE: A Multi-LLM Orchestrated AI Tutor for Secure Software Development Education with Neurodivergent-First Design
• Abstract summary: This paper presents SYNAPSE, a publicly deployed adaptive tutoring platform for Java, Python for cybersecurity, and secure software development.<n>It exposes eighteen always-visible accessibility features and anchors practice in ShopSecure, a deliberately vulnerable web application.<n>A feasibility pilot with nineteen participants returned a System Usability Scale score of 76.4 and engagement of 4.2/5, with comparable cognitive-load levels across cohorts.
• Score: 0.34376560669160394
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Securing the Sandbox: A Rootless Containerized Framework for Process-Oriented Monitoring in Computer Graphics Education [0.0]
  This paper presents VISMATIC, a secure, low-cost framework designed to resolve this tension between process-oriented monitoring and infrastructure security.<n>By pairing robust environment isolation with explicit user-interaction tracking at the API level, VISMATIC captures authentic programming behaviors without exposing the underlying host system.
  arXiv  Detail & Related papers  (2026-06-04T09:33:23Z)
• SentinelSphere: Integrating AI-Powered Real-Time Threat Detection with Cybersecurity Awareness Training [0.0]
  We present SentinelSphere, a platform driven by artificial intelligence that unifies machine learning-based threat identification with security training powered by a Large Language Model (LLM)<n>The detection module uses an Enhanced Deep Neural Network (DNN) trained on the CIC-IDS 2017 and CIC-DDoS 2019 benchmark datasets, enriched with novel HTTP-layer feature engineering that captures application level attack signatures.<n>For the educational component, we deploy a quantised variant of Phi-4 model (Q4_K_M), fine-tuned for the cybersecurity domain, enabling deployment on commodity hardware requiring only 16 GB of RAM without dedicated GPU resources
  arXiv  Detail & Related papers  (2026-04-08T09:56:53Z)
• Secure Code Generation via Online Reinforcement Learning with Vulnerability Reward Model [60.60587869092729]
  Large language models (LLMs) are increasingly used in software development, yet their tendency to generate insecure code remains a major barrier to real-world deployment.<n>We propose SecCoderX, an online reinforcement learning framework for functionality-preserving secure code generation.
  arXiv  Detail & Related papers  (2026-02-07T07:42:07Z)
• Just Ask: Curious Code Agents Reveal System Prompts in Frontier LLMs [65.6660735371212]
  We present textbftextscJustAsk, a framework that autonomously discovers effective extraction strategies through interaction alone.<n>It formulates extraction as an online exploration problem, using Upper Confidence Bound--based strategy selection and a hierarchical skill space spanning atomic probes and high-level orchestration.<n>Our results expose system prompts as a critical yet largely unprotected attack surface in modern agent systems.
  arXiv  Detail & Related papers  (2026-01-29T03:53:25Z)
• Toward Training Superintelligent Software Agents through Self-Play SWE-RL [66.11447353341926]
  Self-play SWE-RL is a first step toward training paradigms for superintelligent software agents.<n>Our approach takes minimal data assumptions, only requiring access to sandboxed repositories with source code and installed dependencies.<n>Our results, albeit early, suggest a path where agents autonomously gather extensive learning experiences from real-world software repositories.
  arXiv  Detail & Related papers  (2025-12-21T00:49:40Z)
• ASTRA: Autonomous Spatial-Temporal Red-teaming for AI Software Assistants [21.35387344588118]
  ASTRA is an automated system designed to uncover safety flaws in AI-driven code generation and security guidance systems.<n>ASTRA finds 11-66% more issues than existing techniques and produces test cases that lead to 17% more effective alignment training.
  arXiv  Detail & Related papers  (2025-08-05T21:57:52Z)
• Report on NSF Workshop on Science of Safe AI [75.96202715567088]
  New advances in machine learning are leading to new opportunities to develop technology-based solutions to societal problems.<n>To fulfill the promise of AI, we must address how to develop AI-based systems that are accurate and performant but also safe and trustworthy.<n>This report is the result of the discussions in the working groups that addressed different aspects of safety at the workshop.
  arXiv  Detail & Related papers  (2025-06-24T18:55:29Z)
• Teaching DevOps Security Education with Hands-on Labware: Automated Detection of Security Weakness in Python [4.280051038571455]
  We introduce hands-on learning modules that enable learners to be familiar with identifying known security weaknesses. To cultivate an engaging and motivating learning environment, our hands-on approach includes a pre-lab, hands-on and post lab sections.
  arXiv  Detail & Related papers  (2023-08-14T16:09:05Z)
• Vulnerability Detection Using Two-Stage Deep Learning Models [0.0]
  Two deep learning models were proposed for vulnerability detection in C/C++ source codes. The first stage is CNN which detects if the source code contains any vulnerability. The second stage is CNN-LTSM that classifies this vulnerability into a class of 50 different types of vulnerabilities.
  arXiv  Detail & Related papers  (2023-05-08T22:12:34Z)
• Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning [63.45532264721498]
  Self-supervised learning is an emerging technique to pre-train encoders using unlabeled data. We perform first systematic, principled measurement study to understand whether and when a pretrained encoder can address the limitations of secure or privacy-preserving supervised learning algorithms.
  arXiv  Detail & Related papers  (2022-12-06T21:35:35Z)
• Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning [91.13113161754022]
  We introduce timing-based adversarial strategies against a DRL-based navigation system by jamming in physical noise patterns on the selected time frames. Our experimental results show that the adversarial timing attacks can lead to a significant performance drop.
  arXiv  Detail & Related papers  (2020-02-20T21:39:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.