Critical co-discovery approaches in teacher education can enhance educators' AI literacy by facilitating collaborative understanding of AI concepts, ethical implications, and pedagogical applications.

Objective: The main goal of this study was to explore how a critical co-discovery approach can facilitate educators' engagement with AI in education (AIEd), focusing on identifying key components of AI literacy that can be addressed through this approach and understanding how it fosters educators' active and reflective engagement with AI in educational settings.

Methods: The researchers implemented a critical co-discovery approach within a newly designed online course titled "Introduction to Artificial Intelligence in Education," offered in Fall 2023 at a Midwest U.S. research university. This four-module, one-credit online course included both synchronous and asynchronous components. Nine educators participated in the study, including pre-service teachers, in-service teachers, an instructional designer, and a PhD student. The critical co-discovery approach involved five key activities:

1. Collaborative Collage: Educators used AI image generators to create visual representations of future AIEd
2. Teachable Machine: Participants explored machine learning concepts through hands-on training of algorithms
3. Polarity Management: Educators examined contradictions in AI integration through mapping exercises
4. Experimenting with Algorithmic Bias: Participants analyzed biases in generative AI tools
5. The Hype Cycle of AI: Educators categorized AI applications using the Gartner Hype Cycle model

Data were collected from various artifacts created during these activities, including images, reflections, and discussion board posts. The researchers performed systematic artifact analysis to examine how these activities engaged educators with five components of AI literacy: foundations, ethics, pedagogy, empowerment, and societal impact.

Key Findings:

• Critical co-discovery activities helped educators collaboratively explore fundamental AI concepts while highlighting concerns about biases within AI systems through experiential learning.
• Ethical awareness was developed through collective inquiry about various ethical concerns, such as privacy issues, transparency, accountability, and the risk of perpetuating inequalities.
• The Hype Cycle and Polarity Management activities helped educators evaluate the pedagogical value of AI tools and distinguish between overhyped tools and those with genuine educational benefits.
• Educators expressed tension between integrating AI in educational settings and fears about AI potentially diminishing their professional roles; however, many viewed AI as a supportive tool rather than a threat.
• Discussions revealed educators' concerns about AI's societal implications, including its unpredictable impacts on jobs, the economy, culture, and education.
• While educators engaged with immediate practical concerns, their exploration of deeper sociopolitical aspects of AI remained limited, highlighting the need for sustained engagement with AI literacy.

Implications: The study offers several valuable contributions to the field of AI in education:

1. It proposes critical co-discovery as an effective pedagogical approach that empowers educators to construct comprehensive understanding of generative AI through hands-on, reflective experiences.
2. The findings highlight that AI literacy requires iterative and progressive learning experiences that evolve over time, not just short-term exposure.
3. The study demonstrates that teacher education programs should extend beyond teaching immediate practical applications of AI to cultivate deeper understanding of broader sociopolitical dimensions.
4. The approach helps alleviate educators' anxiety about AI adoption by fostering an environment that prioritizes exploration over enforcement.
5. The research provides practical, adaptable activities that teacher educators can utilize across various contexts to foster AIEd literacy.

Limitations: The study had several limitations:

• The small sample size (9 participants) and short duration of the course limited the depth of engagement with AI literacy components.
• The research did not differentiate between educators from different backgrounds (pre-service teachers, in-service teachers, instructional designers), which may have varying needs and interactions with AI.
• Educators' discovery of deeper sociopolitical aspects of AI remained limited, with discussions primarily centered on immediate educational contexts rather than broader implications.
• The study's focus on a single course implementation may not capture the long-term development of AI literacy, which requires ongoing engagement.

Future Directions: The researchers suggest several areas for future research:

• Exploring how different educator groups (pre-service teachers, instructional designers, teacher trainers, administrators) engage with AI and their specific needs.
• Designing and implementing sustained, progressive AI literacy development opportunities integrated throughout teacher education programs.
• Investigating how to deepen educators' understanding of the broader sociopolitical dimensions of AI beyond immediate practical applications.
• Developing AI literacy curricula specifically tailored to educators' professional contexts, making them directly relevant to their needs.
• Exploring how critical co-discovery approaches can be adapted for different educational settings and contexts.

Title and Authors: "AI Literacy in Teacher Education: Empowering Educators Through Critical Co-Discovery" by Melis Dilek, Evrim Baran, and Ezequiel Aleman

Published On: 2025

Published By: Journal of Teacher Education