Aspyra: Peer-Based Collaborative Learning Using AI-Driven Roadmaps and Gamified Study Accountability

Abstract

Independent learning environments frequently suffer from inadequate personalization, limited peer interaction, and unclear progression frameworks. This research presents Aspyra, an artificial intelligence-enhanced collaborative platform that integrates customized learning pathways, skill-matched peer connections, streak-based motivation systems, Kanban task organization, and instantaneous communication capabilities. The platform employs large language models to generate adaptive daily learning objectives while connecting learners based on competency alignment. A mutual accountability mechanism through study buddy streaks maintains consistent engagement. Preliminary deployment demonstrated a 67% improvement in daily platform engagement compared to conventional study applications. The system operates on cloud-native infrastructure with real-time data synchronization to enable scalable collaborative learning sessions.

Introduction

Digital transformation in education has expanded opportunities for personalized learning but introduces challenges in engagement, collaboration, and social motivation. Research shows structured peer learning produces significantly better outcomes (effect sizes 0.54–0.88), yet online learners face difficulties such as low completion rates (<15%) and reduced engagement in isolated environments. Modern platforms overwhelm learners with content choices while lacking guidance, social reinforcement, and accountability structures.

Key issues in current online learning systems include fragmented tools, random peer assignment, weak integration of AI personalization with social accountability, and privacy concerns with cloud-based analytics.

Aspyra addresses these gaps through five innovations:

1. LLM-Based Adaptive Planning – personalized learning sequences from goals and proficiency.

2. Skill-Proximity Matching – competency-aware peer pairing for effective collaboration.

3. Reciprocal Streak Accountability – gamified duo-level streaks to maintain engagement.

4. AI-Assisted Task Decomposition – breaking objectives into manageable Kanban tasks.

5. Unified Communication Infrastructure – real-time messaging embedded in collaboration contexts.

Research insights highlight that:

• Competency-proximate peer learning improves engagement and achievement.

• AI-driven personalized pathways increase completion rates but are ineffective without social accountability.

• Gamification, especially streak-based systems, boosts engagement up to 41%.

• Kanban methods reduce cognitive load and improve task throughput.

• Synchronous interaction increases learner satisfaction slightly over asynchronous methods.

System architecture: Aspyra uses a modular, service-oriented design supporting real-time collaborative learning with data sovereignty, browser compatibility, and no specialized hardware requirements.

Conclusion

This study introduced Aspyra, an AI-enhanced peer learning platform that integrates adaptive roadmap generation, competency-aware peer pairing, and gamified accountability to address motivation and fragmentation issues in self-directed education. By unifying personalized guidance, study-duo accountability, and real-time collaboration within a cohesive architecture, Aspyra enables structured, engaging, and socially reinforced learning experiences. Pilot evaluations revealed a 67% increase in daily active usage and sustained study-duo streaks averaging five or more days, validating the platform’s capacity to enhance engagement through reciprocal accountability and AI-driven task planning. The system’s service-oriented architecture—combining authenticated REST and WebSocket interfaces, AI orchestration pipelines, and normalized event analytics—supports scalable and resilient deployment across varied learner contexts. Future work will focus on expanding longitudinal studies to evaluate retention and skill transfer, improving explainability in AI-generated roadmaps, and refining peer-matching algorithms for dynamic balance. Enhancements such as mobile accessibility, adaptive interface design, and retrieval-augmented generation for transparent AI sourcing will further extend its educational impact. Overall, Aspyra demonstrates that integrating intelligent personalization with social accountability can substantially improve learner consistency, motivation, and progress, establishing a promising framework for the next generation of collaborative learning systems.

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Copyright

Copyright © 2025 Preeti Kalra, Nishant , Ashutosh Jha, Snehit Pandey. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.