A scaffolding based pedagogy for AI assisted education

How can you design a pedagogy with generative AI in mind? ie with the idea that you will work with AI during the course as an assistant or a copilot?

Designing a pedagogy with generative AI in mind, where the AI acts as an assistant or copilot, requires a blend of traditional instructional design principles with emerging AI capabilities.?

Lets first consider this from an educational scaffolding perspective as a traditional instructional design.?

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What is scaffolding in relationship to education

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Scaffolding in education refers to a teaching method where the teacher provides successive levels of temporary support to help students achieve higher levels of understanding and skill development. This technique, based on the work of psychologist Lev Vygotsky , focuses on helping students progress through tasks they may not be able to complete independently.

Key Features of Educational Scaffolding:

1. Temporary Support: Scaffolding is designed to be gradually removed as the student gains competence, much like physical scaffolding is taken down when a building is structurally sound.
2. Zone of Proximal Development (ZPD): Vygotsky's concept is central to scaffolding. The ZPD is the gap between what a learner can do independently and what they can achieve with help. Scaffolding helps students work within this zone.
3. Breaking Tasks into Smaller Steps: Complex tasks are broken into manageable parts, allowing students to build confidence and understanding gradually.
4. Active Engagement: Teachers offer guidance, ask guiding questions, and model behaviors to engage students actively in their learning process.
5. Gradual Release of Responsibility: As students demonstrate increased proficiency, the teacher slowly reduces the level of support, allowing the learner to take more control over their learning.

?scaffolding techniques that can be applied across various subjects include:

1. Modeling: The teacher demonstrates the desired task, showing students exactly how to approach and complete it. This can include techniques like "think-alouds," where the teacher verbalizes their thought process as they work through a problem or read a text.
2. Breaking Tasks into Smaller Steps: Complex activities are divided into manageable segments. Each step is taught progressively, allowing students to build understanding before moving on to the next phase.
3. Using Visual Aids: Tools like graphic organizers, charts, and diagrams help students visually process and organize information. These aids act as "training wheels" that guide students until they can work independently.
4. Tapping into Prior Knowledge: Teachers activate what students already know by connecting new content to their prior experiences. This helps students feel more engaged and makes the new material more relevant.
5. Pre-Teaching Vocabulary: Introducing key vocabulary before tackling a complex text or topic helps students avoid confusion and better understand the material.
6. Checklists and Rubrics: These tools provide students with clear expectations and a step-by-step guide to follow during assignments, helping them stay organized and focused.
7. Collaborative Learning: Engaging students in group work or peer teaching helps them scaffold one another's learning, fostering a deeper understanding of the material.
8. Providing Time to Process: Allowing students to take time to reflect and discuss new information helps solidify their understanding and reduces cognitive overload.
9. Guided Practice (I Do, We Do, You Do): The teacher first demonstrates the task ("I Do"), then works together with students ("We Do"), and finally allows students to practice independently ("You Do").
10. Use of Anchor Charts: These are co-created with students during lessons and serve as a visual reference throughout the learning process, reinforcing key concepts.

Scaffolding follows a progression of:

• Introduction and modeling
• Guided practice with feedback
• Independent practice

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How could scaffolding be enhanced by AI in educational settings??

Now, the next question is - how can AI enrich scaffolding?

Define the Learning Objectives: Establish learning goals that specify how AI will support educational objectives, such as enhancing skill-building, aiding in content creation, or providing personalized feedback. Encourage student agency in the collaboration by fostering critical thinking and decision-making skills, enabling students to critically evaluate AI-generated outputs.

Blended Learning Model for AI and the tutor: Combine traditional instruction with AI-enhanced activities to create a hybrid learning environment. Instructors can focus on teaching core concepts, while AI assists with application, practice, and personalized support. Implement a flipped classroom approach by using AI to provide preparatory materials before class, allowing in-person sessions to focus on deeper discussions and problem-solving.

AI-Powered Personalization: Utilize AI to tailor learning paths based on individual student progress, strengths, and weaknesses. The AI can dynamically recommend materials, assessments, or practice exercises. Design activities that offer individualized feedback from both AI and instructors, with AI providing instant, detailed suggestions in areas like writing or problem-solving.

Promote Critical AI Literacy: Educate students about the limitations and biases of AI to enhance their critical AI literacy. Develop assignments that require them to critically analyze AI outputs and discuss ethical implications. Include modules on the ethical use of AI in learning and professional contexts, fostering discussions on when to rely on AI and when to exercise human judgment.

AI in Formative and Summative Assessment: Leverage AI to provide continuous formative feedback during assignments, helping students iterate and improve their work before final submission. In summative assessments, use AI to support grading in objective tasks like coding or grammar, while ensuring human oversight for subjective evaluations like essays or projects. Encourage self-assessment and reflection by having students use AI-generated reports or summaries to track their learning journey.

Iterative and Exploratory Learning: Design tasks that promote creativity and prototyping by using AI to generate multiple drafts or solutions. This approach allows students to explore various ideas quickly and refine the most promising ones. Encourage rapid experimentation with AI tools in coding, design, or writing, enabling students to test different approaches and see immediate results.

?Continuous Improvement through AI: Employ AI-driven analytics to track student progress and adapt the course in real time. AI can highlight areas where students struggle and recommend adjustments to instruction. Continuously refine the curriculum based on AI analysis of student performance and engagement, making data-driven improvements to content and delivery.

?AI as a Thought Partner for Deep Learning: Philosophical Discussions with AI: Engage students in philosophical debates or ethical discussions about the future of AI, with the AI acting as a conversation partner. The AI could provide counterarguments, data, or historical examples, pushing students to think deeply about AI's role in society. Future Problem-Solving Workshops: Organize workshops where AI helps students simulate solutions to future global challenges (e.g., climate change, inequality, or AI governance). The AI can suggest strategies or highlight existing technological constraints, enabling students to think critically about innovation.

AI-Enhanced Creativity and Innovation:? AI-Assisted Creative Projects: Encourage students to collaborate with AI on creative projects such as writing, art, music, or entrepreneurship. AI could generate ideas, outlines, or prototypes, which students refine, enabling them to focus on high-level creativity.? Designing the Future with AI: Introduce a course module where students envision the future with AI-driven technologies, from healthcare to governance. AI could assist in generating scenarios based on current trends, allowing students to craft responses or policies for these potential futures.

AI as a Mentor and Lifelong Learning Facilitator:? AI as a Personal Mentor: Use AI to provide personalized mentoring for each student, helping them reflect on their learning journey, strengths, and weaknesses. AI could offer resources, suggest areas for improvement, and track individual growth. Lifelong Learning Ecosystem: Design pedagogy around the idea that learning never stops. AI could guide students in continuously updating their knowledge even after course completion by suggesting further readings, projects, or certifications tailored to their evolving interests.

Collaborative Intelligence Models:? Human-AI Collaboration Exercises: Introduce coursework where students and AI collaborate to solve complex problems (e.g., scientific research, business strategy). AI could analyze large datasets, propose hypotheses, or simulate outcomes, while students interpret and critically assess the AI’s contributions. Collective AI Intelligence Platforms: Encourage students to build collaborative knowledge bases with AI’s help, where AI aggregates information from all students’ work to create shared learning resources. This would mirror a collective intelligence platform, where humans and AI co-create content dynamically.

AI-Empowered Ethical Dilemmas: AI-Guided Ethical Case Studies: Design a series of ethical dilemmas where AI plays an integral role in proposing solutions, each with different trade-offs. Students could assess AI’s suggestions, explore the social implications, and decide whether or not to follow AI guidance. AI and Human Responsibility Projects: Create a course unit where students work with AI to identify and solve ethical problems in areas like AI bias, privacy, and data security. This allows for discussion on how AI can support ethical decision-making without removing human accountability.

Project-Based Learning with AI: AI for Social Good Challenges: Challenge students to use AI in addressing real-world social issues, such as education inequality or healthcare access. AI could generate project ideas, analyze relevant data, and provide insights to guide students through developing innovative solutions. AI-Powered Research Projects: Allow students to embark on research projects with AI assisting in data gathering, analysis, and synthesis. AI could help them explore new frontiers of knowledge by automating mundane tasks, enabling students to focus on original thought and innovation.

Building AI-Literate Citizens:? AI Literacy for All: Teach students the foundational concepts of AI, its limitations, and its societal impact, empowering them to navigate a future where AI is deeply integrated into many aspects of life. Ensure students understand how to interpret AI outputs critically, how to ethically use AI, and how AI influences decision-making. AI in Public Policy Simulation: Use AI to simulate policymaking scenarios in government, where students can explore the trade-offs AI-driven policy decisions could create. AI could generate alternative policy routes, forecast outcomes, and provide models for debate.

AI and Uncertainty:? AI-Assisted Strategic Foresight: Introduce foresight and scenario planning exercises where AI helps students explore uncertain futures. AI could propose future trends based on current data, and students assess how to prepare for these uncertainties. AI for Ambiguous Problem-Solving: Use AI to assist in solving ill-defined or ambiguous problems, helping students refine the problem scope and offering multiple possible solutions. This would build their resilience in working with incomplete or evolving information.

Developing AI-Enhanced Leadership Skills: AI-Driven Leadership Simulations: Create leadership scenarios where students manage teams or organizations with AI as a support tool. Students could make high-level decisions while AI provides analysis, forecasts, and recommendations. AI and Emotional Intelligence: Use AI tools to help students build emotional intelligence by engaging in simulations that model human behavior. AI could offer feedback on interpersonal dynamics, conflict resolution, and empathy-building exercises.

Leveraging Generative AI for Continuous Reflection: AI-Powered Reflective Journals: Encourage students to maintain reflective journals with AI’s assistance. AI could offer suggestions on how to frame reflections, track recurring themes, or propose actionable insights based on personal growth trajectories. AI-Supported Learning Dashboards: Offer students a personalized dashboard powered by AI, where they can track their learning progress, review AI-generated summaries of their key achievements, and receive recommendations on next steps for improvement.

Conclusion

Both these ideas are not new (Scaffolding and AI in education). But the combination provides a framework for the immediate future. I am working in this space. If anyone else is , happy to discuss. The idea of scaffolding is familiar to educators and thus provides a common framework for introducing AI in education.