Engaging Teachers to Co-Design Integrated AI Curriculum for K-12 Classrooms

Title of the Paper

Engaging Teachers to Co-Design Integrated AI Curriculum for K-12 Classrooms

Paper Information

• Subject Area: Artificial Intelligence Education and Human-Computer Interaction Design
• Keywords: Artificial Intelligence, K-12 Education, Co-Design, Learning Tools, Teacher Engagement, Educational Technology, Data Integration, Ethical Practices, Teaching Innovation

Research Background and Issues

• Problems and Challenges:

  • Current K-12 AI education curricula and tools do not fully align with teachers' values and teaching contexts.
  • AI education tools are still primarily limited to computer science rather than being widely applied to core subjects like English, mathematics, and social sciences.
  • Many teachers lack sufficient understanding of AI, making it difficult to integrate AI into existing curricula.

• Significance:

  • As AI technology rapidly integrates into daily life, it is crucial to cultivate students' abilities to recognize, understand, apply, and critically think about AI.
  • Expanding AI education can expose more students in non-computer science disciplines to key technologies, reducing the digital divide.

• Research Motivation and Work:

  • Employing a value-sensitive design approach to create classroom-appropriate AI curricula and tools centered on teachers.
  • Using co-design to involve teachers from diverse backgrounds in curriculum development, enhancing its relevance and effectiveness.

Solution

• Research Methods:

  • Organizing a two-day remote co-design workshop involving 15 K-12 teachers in group activities.
  • On the first day, participants learn basic AI concepts and teaching tools; on the second day, they explore teaching ethics and design interdisciplinary AI-integrated curricula.

• Innovations:

  • Integrating AI education into core subjects such as social sciences, English as a Second Language (ESL), and literacy education.
  • Collecting teachers' values and practical needs for curriculum design and providing mechanisms for ethical reflection.

• Implementation Steps and Techniques:

  • Conducting card-sorting activities to help teachers understand potential applications of AI in various subjects.
  • Designing curricula in collaborative groups, combining AI tools like Teachable Machine and ML4Kids with specific educational activities.
  • Introducing ethics and data reflection to encourage teachers to adopt AI in non-technical subject learning.

Research Outcomes

• Specific Outcomes:

  • Drafted three integrated AI curriculum prototypes, including:
    • Sociology curriculum: Interpreting the impact of data on government policies.
    • Special education curriculum: Using AI to learn vocabulary.
    • ESL curriculum: Creating an AI-assisted application for speech pronunciation.
  • Teachers reported increased confidence in teaching AI after the workshop and recognized that AI education extends beyond computer science.

• Comparison with Existing Solutions:

  • Expanded AI curricula from computer science to core subjects, reaching a broader student population.
  • Strengthened teacher involvement in curriculum development rather than relying solely on researchers.

• Experimental/Evaluation Results:

  • Teachers completed initial curriculum drafts showcasing various methods for data-driven, ethical reflection and curriculum integration.
  • Teachers' familiarity with AI increased from an average of 4.8 to 5.8 (on a 7-point scale) during the workshop.

• Limitations and Future Directions:

  • The curriculum designs were not tested with student groups, leaving the specific effects of the curricula unverified.
  • The small sample size necessitates larger-scale studies to validate the generalizability of the findings.
  • The limited workshop duration prevented some teachers from fully exploring the tools; future activities should be extended and encourage deeper collaboration among teachers.

Design Recommendations

• Design Goals:
  • Develop Evaluation Mechanisms: Add features to support the assessment of student learning outcomes.
  • Design Interactive Mechanisms: Stimulate student interest and enable innovative teaching beyond traditional participation methods.
  • Simplify Teaching and Tool Integration: Enhance the flexibility and cross-disciplinary applicability of tools.
  • Support Collaborative Learning: Provide mechanisms to promote group learning and joint exploration among students.
  • Integrate Data Foundations: Connect data from core subjects with AI tools to improve teaching effectiveness.
  • Promote Critical Reflection: Support students in deeply reflecting on teaching content and AI methods.
  • Advance Ethical Discussions: Offer resources and methods to support ethical analysis, helping teachers address challenges in moral discussions.

Conclusion

This study tested how AI education can be integrated into K-12 core curricula through remote co-design activities, while also inspiring teachers' creativity and active participation. The work expands the scope of AI education, demonstrates the value of creating diverse curricula, and provides practical guidance for future co-design efforts.