C15: From Climate Shock to Action: Designing AI-Powered Learning Experiences for Sustainable Futures

Abstract

This introductory course explores how artificial intelligence and future foresight can be applied to design emotionally intelligent, sustainability-driven learning ecosystems. Drawing on empirical research from UAE higher education contexts, the session demonstrates how Competency-Based Learning (CBL) frameworks combined with AI tools can transform passive awareness of sustainability into proactive agency.

Participants will be introduced to the Climate Shock Matrix (CSM), an innovative model developed to map emotional and behavioral responses to sustainability crises and learn how AI-powered applications such as CSM Quest, Foresight Challenge, and Classrooms of Tomorrow GPT support educators in guiding learners from unawareness and anxiety toward confident, action-oriented engagement.

Through the lens of Human-Computer Interaction (HCI), the course examines design principles for emotionally responsive learning systems that integrate foresight methodologies, SDG 4 alignment, and local cultural factors. It emphasizes how AI can ethically augment the teaching process while reinforcing empathy, reflection, and self-efficacy rather than replacing human judgment.

Participants will engage in interactive foresight games, guided design activities, and short collaborative simulations to prototype interventions that move learners through the four CSM stages: Unawareness, Anxiety, Emerging Awareness, and Knowledge-Action. The course positions educators and designers as facilitators of climate resilience and agents of responsible AI adoption in education.

By merging the science of learning with the art of foresight, this course equips attendees with a practical framework and digital toolkit to design classroom, community, or institutional innovations that connect emotional intelligence, AI, and sustainability for long-term impact.

Course Content

Content Overview

1. Introduction to AI and Foresight in Learning Design
   • Overview of applied foresight frameworks in HCI and education
   • The ethics of AI in human-centered learning
2. The Climate Shock Matrix Framework
   • Emotional-behavioral mapping of sustainability awareness
   • Case studies from UAE higher education
3. Competency-Based Learning and AI Integration
   • Linking CBL with SDGs and student agency
   • Tools: CSM Quest, Foresight Challenge, Classroom of Tomorrow GPT
4. Designing Emotionally Intelligent Systems
   • Leadership mapping (coaching, servant, transformational)
   • Building digital empathy and reflective engagement
5. Implementation and Evaluation
   • Measuring change in awareness, collaboration, and self-efficacy

Hands-On Component

• Activity 1: CSM Foresight Challenge – participants play an interactive AI-powered scenario game to identify their current climate-shock stage.
• Activity 2: Action Path Design Sprint – teams use the Classroom of Tomorrow GPT to co-design short interventions targeting specific learner profiles.
• Activity 3: Reflection Matrix Mapping – participants use provided templates to plan transitions from anxiety to action in their context.

Course Goals

The course aims to equip educators, researchers, and designers with the theoretical grounding and practical tools to:

1. Integrate AI and foresight methodologies into learning design to foster sustainable behavior and critical thinking.
2. Apply the Climate Shock Matrix (CSM) as a framework for emotional engagement and agency in sustainability education.
3. Develop competency-based, emotionally intelligent learning environments aligned with SDG 4 (Quality Education) and SDG 13 (Climate Action).
4. Use human-centered AI systems to support reflection, collaboration, and ethical innovation in classrooms and digital learning ecosystems.

Learning Objectives

By the end of this course, participants will be able to:

• Explain the theoretical and psychological foundations of the Climate Shock Matrix model.
• Design foresight-based interventions to move learners through the four stages of climate shock (Unawareness → Anxiety → Emerging Awareness → Knowledge & Action).
• Employ AI-powered tools (CSM Quest, Foresight Challenge, Classroom of Tomorrow GPT) for designing and evaluating learning activities.
• Map leadership and motivation strategies (coaching, servant, transformational) to student engagement phases.
• Develop an action plan integrating foresight, emotional intelligence, and AI ethics within their institutional context.

Topics to be covered:

1. AI and Foresight Fundamentals
   • Role of AI in education and learning sciences
   • Foresight methods: scenario planning, backcasting, and futures wheel
2. Emotional Intelligence and Climate Psychology
   • Understanding emotional drivers of climate engagement
   • The Climate Shock Matrix (CSM) model
3. Competency-Based Learning (CBL) and Sustainability
   • Linking SDG frameworks with local and institutional contexts
   • Case studies: UAE higher education and COP28 student projects
4. Designing Human-Centered AI Learning Systems
   • HCI perspectives on empathy and ethics
   • Integrating generative AI tools responsibly
5. Foresight Games and AI Applications
   • Using the CSM Quest, Foresight Challenge, and GPT-based design agents
6. Leadership, Agency, and Action Pathways
   • Leadership styles and motivation strategies across CSM stages
   • Building climate confidence and cross-sector collaborations
7. Implementation and Evaluation
   • Assessment metrics for engagement and transformation
   • Reflection, feedback, and AI-enabled dashboards

Hands-On Component (Detailed Breakdown)

1. CSM Foresight Challenge (30 minutes)

Objective: Diagnose participants’ current position within the Climate Shock Matrix (Unawareness, Anxiety, Emerging Awareness, Knowledge-Action).
Activity:

• Participants play the Foresight Challenge (Gemini AI link) individually.
• Each profile generates a personalized climate-awareness trajectory.
• Facilitator debriefs emotional and behavioral insights using visual dashboards.
  Outcome: Participants identify personal and institutional “climate shock” barriers.

2. CSM Quest Workshop (45 minutes)

Objective: Design foresight-informed interventions for classroom or community contexts.
Activity:

• Participants use the CSM Quest ChatGPT Canvas to simulate learner engagement journeys.
• In teams of three, they select an SDG (e.g., climate, education, health) and design a short activity addressing one CSM stage.
• AI generates potential actions, media prompts, and student reflection questions.
  Outcome: Co-created learning activity ready for pilot testing.

3. The Classroom of Tomorrow GPT Sprint (60 minutes)

Objective: Translate foresight and emotional engagement into competency-based course design.
Activity:

• Teams use the Classrooms of Tomorrow GPT to design a microlearning prototype.
• Input parameters: skill focus, emotional goal, AI interaction style, SDG link, and reflection prompt.
• Output: 5-minute activity generator + foresight-based lesson sequence.
  Outcome: Prototype AI-enhanced learning experience with measurable sustainability competencies.

4. Reflection and Leadership Mapping (30 minutes)

Objective: Link emotional stages to leadership models (transformational, servant, democratic).
Activity:

• Participants map their CSM profiles to leadership archetypes using a guided worksheet.
• Discussion: “Which leadership approach best drives climate action in my institution?”
  Outcome: Individual action plan template for institutional integration.

5. AI Dashboard Simulation (30 minutes)

Objective: Visualize learner engagement and foresight outcomes.
Activity:

• Facilitator demonstrates an AI analytics dashboard prototype (mock interface).
• Participants practice defining metrics for measuring engagement and emotional transition.
  Outcome: Framework for integrating foresight indicators and self-efficacy metrics into learning analytics.

Outcomes

At the end of the course, participants will produce:

1. A personal CSM profile identifying their readiness for sustainability action.
2. A foresight-informed learning intervention co-created with AI.
3. A prototype AI dashboard or evaluation plan for tracking learner transformation.
4. An individual reflection statement linking emotional intelligence, foresight, and leadership.