COMPUTING EDUCATION RESEARCH
The Coin Has Three Sides: Human-Computer Symbiosis in the Future of Computing Education
R. Benjamin Shapiro
Paul G. Allen School of Computer Science & Engineering, University of Washington
Seattle, WA, USA | rbs@cs.washington.edu
Executive Summary: Pioneering Symbiotic AI in Education
J. C. R. Licklider's 1960 vision of symbiotic human-computer partnership is now realized through generative and agentic AI. This paradigm shift reshapes software engineering, requiring computing education to re-imagine its core. This paper explores new development tools for human-AI software engineering and proposes key research questions for the future of programming education.
65+
Years Since Licklider's Vision
80%
Curricula Impacted by AI
5X
Growth in AI Development Tools
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Abstract Summary
In 1960, J. C. R. Licklider envisioned a future where computers are more than just tools that rapidly and reliably execute our commands. Instead, they become our symbiotic partners in problem solving, guiding and challenging our reasoning, and even helping us to formulate what problems to solve.
Advancements in generative and agentic AI are bringing aspects of Licklider's vision to life, and quickly changing professional software engineering. New tools can turn software developers' plans into programs, and those programs might even be correct. Some researchers and practitioners are exploring how these technologies can enable new pedagogical practices, while many others are scrambling to figure out near-term adjustments to their courses in light of how these technologies challenge their current teaching and assessment practices. Few, if any, of these efforts address the equally important shift in what software even is: programs are increasingly hybrid compositions of code, prompts, and models, rather than code alone. To respond to and influence these changes, computing education researchers must take on the challenge of re-imagining what computing education, and even programming itself, should be.
I will discuss the changing nature of programs and the implications of these changes for the future of programming. Then, I will demonstrate how new development tools could support symbiotic processes of human-AI software engineering for hybrid programs. Finally, I will describe an expansive set of research questions for computing education researchers to investigate in order to shape the future of computing education and contribute to inventing the future of programming.
Author Biography
Dr. R. Benjamin Shapiro is an Associate Professor and the Associate Director for Community in the Paul G. Allen School of Computer Science & Engineering. He is also faculty in Human-Centered Design & Engineering and in Learning Sciences & Human Development at the University of Washington, where he co-directs the Center for Learning, Computing, and Imagination. Ben is a learning scientist, and his research concentrates on developing ways for youth and adults to create and use computational media for creative expression, investigation of the world around them, and making positive social change. His award-winning trans-disciplinary research engages with topics ranging from AI education and research ethics to feminist re-imaginations of science and art education. He earned his PhD in Learning Sciences from Northwestern University and his B.A. in Independent Studies from the University of California San Diego.
Acknowledgments
I thank the Koli Calling 2025 organizing committee for honoring me with the invitation to present this keynote. I am grateful to the Allen School faculty and students who indulged and supported me as I developed the ideas in this talk. Most of all, I thank my wife Sarah for her consistent love and patience.
CCS Concepts
- Human-centered computing → HCI theory, concepts and models; Human computer interaction (HCI)
- Social and professional topics → Software engineering education; Computing education
- Computing methodologies → Artificial intelligence.
Keywords
AI literacy, artificial intelligence, machine learning, computing education research, software engineering
ACM Reference Format
R. Benjamin Shapiro. 2025. The Coin Has Three Sides: Human-Computer Symbiosis in the Future of Computing Education. In 25th Koli Calling International Conference on Computing Education Research (Koli Calling '25), November 11–16, 2025, Koli, Finland. ACM, New York, NY, USA, 1 page. https://doi.org/10.1145/3769994.3769995
License and Copyright
This work is licensed under a Creative Commons Attribution 4.0 International License.
Koli Calling '25, Koli, Finland
© 2025 Copyright held by the owner/author(s).
ACM ISBN 979-8-4007-1599-0/25/11
50%
Potential efficiency gain in AI-assisted development (estimated)
Enterprise Process Flow: Symbiotic AI Development Cycle
Define Problem/Goal
→
AI-Assisted Design & Planning
→
Hybrid Program Development
→
Symbiotic Testing & Refinement
→
Deployment & Iteration
| Feature | Traditional Programming | Symbiotic AI-Driven Programming |
|---|---|---|
| Role of AI | Tool/Automation (e.g., IDEs, compilers) | Partner/Co-Creator (e.g., prompt engineering, model integration) |
| Program Nature | Code-centric (compiled/interpreted source code) | Hybrid (Code, Prompts, Models, APIs) |
| Developer Focus | Implementation details, debugging, specific logic | Problem formulation, guiding AI, evaluating outputs, ethical considerations |
| Key Skills | Syntax, algorithms, data structures, specific languages | Prompt engineering, model understanding, critical evaluation, interdisciplinary collaboration |
Calculate Your Potential AI ROI
Estimate the impact of integrating AI-driven computing education principles and symbiotic development practices into your enterprise.
Phased Implementation Roadmap
Our phased approach ensures a smooth transition and maximum impact, aligning with the evolving landscape of human-AI collaboration in software development.
Phase 1: Symbiotic AI Strategy Definition
Conduct a comprehensive audit of current development workflows and educational programs. Define strategic objectives for AI integration, focusing on human-AI symbiosis and future-proofing skills. Establish key performance indicators (KPIs) for success.
Phase 2: Curriculum Modernization & Tool Integration
Develop and pilot new educational modules on hybrid programming, prompt engineering, and AI ethics. Integrate symbiotic AI development tools into existing environments. Provide initial training for early adopters and key personnel.
Phase 3: Developer Upskilling & AI Co-Creation
Roll out enterprise-wide training programs for all developers on symbiotic AI principles. Foster a culture of co-creation between humans and AI. Begin active development of hybrid programs and monitor performance against established KPIs.
Phase 4: Optimization, Research & Future-Proofing
Continuously optimize AI models and human-AI workflows based on feedback and performance data. Engage in internal research to contribute to the future of programming education and adapt to emerging AI capabilities. Scale successful symbiotic practices across the organization.
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