Collaborative Intelligence in Course Design: A case Study in AI-Human Partnership for Educational Content Creation
Collaborative Intelligence in Course Design: A case Study in AI-Human Partnership for Educational Content Creation
David Lefevre1, Marco Mongiello2
1 Imperial College, London
2 Regent’s University, London
This presentation contributes to the emerging field of collaborative intelligence (co-intelligence) by documenting systematic AI-human partnership on extended educational tasks. Through creating "Introduction to Generative AI for Academic Study" – a 10-12 hour interactive online course for international business students – we demonstrate how structured human-AI collaboration can maintain pedagogical quality while accelerating content development.
Theoretical Framework and Context
Collaborative intelligence (co-intelligence) represents an emerging paradigm where human expertise and AI capabilities are systematically integrated for complex tasks. Current literature lacks operational frameworks for sustained AI-human collaboration beyond simple prompt-response interactions. This work addresses this gap by developing a structured approach to co-intelligence in educational content creation, where the complexity and duration of the task provide a rigorous test case for collaborative methodologies. The educational domain serves as the application context, but the primary focus is advancing co-intelligence practice through systematic documentation of extended human-AI partnership.
Methodology
We developed an eleven-stage collaborative framework with mandatory human intervention at each stage: (1) course specification, (2-3) learning outcome determination, (4-5) structural design, (6-7) content development, (8-11) assessment and review. Over three months and 180 hours, innovations included: iterative prompt libraries enabling reuse and refinement; role-based AI specialisation (professor, editor, subject expert); multi-model validation through cross-platform testing; systematic clarification protocols requiring AI questions before new topics; and structured self-critique generating measurable improvements. Each stage produced documented outputs preventing scope drift and ensuring pedagogical coherence.
Outcomes and Validation
The process generated a complete four-topic course with mobile-optimised interactive screens, embedded formative assessment, and progressive skill development from basic AI literacy to advanced application. Critically, the documented methodology itself constitutes the primary contribution: a replicable framework for extended co-intelligence work that maintains educational standards while leveraging AI efficiency, enabling governance and process control. Preliminary feedback indicates successful knowledge transfer and engagement retention.
Contribution to Co-intelligence Research
This work addresses a gap in co-intelligence literature by demonstrating structured approaches for complex, extended tasks beyond simple prompt-response interactions. The eleven-stage framework provides operational guidance for maintaining human agency while maximising AI capability. Key findings include the necessity of iterative prompt development, the value of multi-stage human checkpoints, and the importance of role-based AI specialization for sustained collaboration quality.
Implications for Educational Practice
The framework offers educators a systematic approach to AI collaboration that preserves pedagogical integrity while achieving scalable content development and process control. This positions educators as co-intelligence architects rather than passive AI consumers, aligning with AIEOU’s participatory design principles and preparing students for thoughtful AI engagement.
The presentation demonstrates specific collaborative techniques and discusses methodological transferability across educational contexts.
View the presentation in full here:
Lefevre, D., & Mongiello, M. (2025, September 17). Collaborative Intelligence in Course Design: A case Study in AI-Human Partnership for Educational Content Creation. AIEOU Inaugural Conference, University of Oxford. Zenodo. https://doi.org/10.5281/zenodo.17290488
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