Future studies can examine the effectiveness of AI-supported practices in enhancing

The gap

Evidence profile

Stated in the future work and conclusions and cells future research sections of the source papers, classified as general, spanning 4 journals. Those papers have been cited 2 times in total.

Supporting evidence — 4 representative gaps

• AI in English language teaching at higher education institutions in Georgia: Usage, training gaps, and institutional support (2026) · The JALT CALL Journal · doi

  Future research should focus on three key areas to enhance our understanding of AI training effectiveness in educational settings: First, a comprehensive assessment of training methodologies is needed to evaluate the relative effectiveness of various training formats, including workshop-based learning, peer-to-peer instruction, online versus in-person training, and project-based learning approaches. Such research would help identify the most effective delivery methods for different learning contexts and teacher needs. This includes investigating the effectiveness of different AI training programs in improving student learning outcomes. Second, researchers should conduct detailed barrier analyses to understand the specific obstacles that prevent trained teachers from applying their knowledge effectively. This includes examining the relationship between different types of training and confidence levels, as well as studying how various support systems impact teacher performance.

  generalstated in future workevidence 5/5
  Keywords: training learning effectiveness different various based peer teacher includes future focus three areas enhance understanding
• Students’ attitudes, challenges, and needs in English reading comprehension: Foundations for AI-integrated corpus-based language pedagogy (2026) · Discover Education · cited 2× · doi

  Future research should address these limitations by involving larger and more diverse participant groups, combining quantitative surveys with qualitative methods such as classroom observation, and implementing experimental or quasi-experimental designs to test the effectiveness of AI-integrated CBLP in practice. Additionally, teacher training and professional development should be investigated to ensure sustainable pedagogical innovation.

  generalstated in conclusionsevidence 5/5
  Keywords: experimental future address limitations involving larger diverse participant groups combining quantitative surveys qualitative classroom observation
• Gender perspectives: Artificial intelligence in academic practice (2026) · Research in Pedagogy · doi

  Future research should examine the impact of AI on student learning outcomes and the potential risks and challenges associated with AI adoption in higher education. - Future research should investigate the factors that influence AI adoption and use in higher education, including gender, discipline, and institutional type. - Future research should explore the development of targeted training and support programs for academic staff to promote AI literacy and equitable access to AI tools and resources.

  generalstated in cells future researchevidence 5/5
  Keywords: future research examine impact student learning outcomes potential
• Exploring the Mathematical Modeling Skills of Pre-Service Elementary Mathematics Teachers Through an Artificial Intelligence Tool: The Case of ChatGPT (2026) · e-Kafkas Eğitim Araştırmaları Dergisi · doi

  Future studies can examine the effectiveness of AI-supported practices in enhancing mathematical modeling skills. - Future studies can investigate the impact of AI on teacher education and student learning outcomes. - Future studies can explore the potential of AI in supporting other aspects of teacher education, such as lesson planning and classroom management.

  generalstated in cells future researchevidence 5/5
  Keywords: future studies examine effectiveness ai-supported practices enhancing mathematical

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