Bridge the AI intention-practice gap through digital infrastructure, professional development, and supportive institutional policies¶

Strategy · All strategies
Evidence · 2 claims (2 for) · 2 studies (2 associational), q2 · 0 of 2 report an effect size · 2 claims rest on one study

Description¶

The article recommends that effective AI integration in science education requires systemic support rather than attitude change alone. It concludes that "effective AI integration requires enhanced digital infrastructure, targeted professional development, and supportive institutional policies to bridge the gap between intention and practice", and that policies should "prioritise capacity building, practical training, and supportive structures". The recommendation rests on the finding that positive perceptions and intentions coexisted with only moderate actual classroom use.

Design Implications¶

Context¶

Requirements¶

• Institutional investment in digital infrastructure and sustained teacher training

Constraints¶

• The article states the findings are specific to the Ghanaian context and generalisation to other countries should be approached with caution

Target Learners¶

• pre-service science teachers in Ghana

Target Learning Goals¶

• sustained classroom integration of AI in science teaching

Claims¶

• Actual classroom use of AI among Ghanaian pre-service science teachers is moderate despite strong intentions, indicating an intention-practice gap [+W]
• Students report gaps in AI training and awareness of institutional AI policies despite widespread adoption [+W]

Related Strategies¶

• Foster communities of practice for AI governance instead of rigid top-down policies
• Pursue inclusive digital strategies, educator professional development, and ethical AI governance for AI in adult education
• Co-create contextualized AI literacy policies via multi-stakeholder task forces with phased roadmaps
• Embed AI policy literacy, peer-led communities of practice, and shared AI resources into professional development
• Adopt a systems approach to AI integration rather than spotlights or gaps
• Institutional governance should establish practical conditions supporting AI literacy, learner agency, and critical evaluation rather than relying on prohibition and detection
• Shift teacher professional development from information-based training to experience-based AI design activities in sustainability contexts

Examples¶

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Key Sources¶

• Amponsah, K. D., Adu-Boahen, J., Commey-Mintah, P., Kumassah, E. K., Ayittey, R. F., Nketsiah, J. (2026). Perceptions And Acceptance of Artificial Intelligence in Science Education Programmes: Voices of Pre-Service Science Teachers. Education Quarterly Reviews, 9(3), 51-65. https://doi.org/10.31014/aior.1993.09.03.724