The quality of AI education literacy training for pre-service teachers directly determines future educational outcomes when AI technologies integrate into all elements and processes of basic education. This study employs theoretical analysis and practical reflection to explore strategies for enhancing AI education literacy training for pre-service teachers. The paper systematically identifies three key challenges in current training practices: structural deficiencies in curriculum design, adaptability crises in textbook development, and difficulties in intelligent transformation of teaching methods. It attributes these challenges to three contradictions: conflicts between curriculum stability and innovation demands, mismatches between textbook standardization and technological diversity, and gaps between educators' current AI education literacy and instructional requirements. The study proposes three solutions. First, it suggests establishing guidelines and revising standards by integrating AI education literacy components into curricula. Second, it advocates policy-driven development of dual-purpose textbook systems combining universal and specialized materials, supported by industry-academia collaboration for dynamic content updates. Third, the paper recommends building university-enterprise integrated intelligent teaching research systems, enhancing faculty professional development, and implementing human-machine collaborative experiential teaching. This research provides concrete support for improving quality of AI education literacy training for pre-service teachers in higher education institutions.
Bao-xun et al. (Tue,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: