The challenge of introducing and integrating GenAI into established teaching activities

The gap

Evidence profile

Sourced from the recommendations and future work and inline gaps and future-work section and stated challenges and stated research gap of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 8 journals. Those papers have been cited 51 times in total.

Supporting evidence — 8 representative gaps

• A Descriptive Content Analysis of Artificial Intelligence Studies with Primary and Pre-Service Primary School Teachers in Türkiye (2026) · Bartın University Journal of Faculty of Education · doi

  Based on these findings, the following recommendations are presented: • The trends and the sustainability of these trends in the use of AI in education can be investigated across various levels (secondary education, higher education, vocational education, etc.). • The balanced use of qualitative and quantitative methods and the encouragement of mixed methods in research can be ensured. In this way, a more comprehensive and holistic understanding of the experiences of in-service and pre-service primary school teachers can be focused on. • Quasi-experimental or action research-based studies, involving the participation of in- service and pre-service primary school teachers in AI-supported instructional applications, can be conducted to determine the concrete impacts on the teaching process. • The greater preference for mixed methods can be recommended, which would allow for the comprehensive evaluation of AI-based educational applications through both quantitative outcomes and participant experiences. It can be recommended that pre-service teachers be more frequently preferred as the study group in research, thereby enabling the evaluation of AI integration in teacher training programs. • • Large-scale studies, including diverse groups of participants (administrators, school counsellors, curriculum development specialists, etc.), can be conducted to increase the generalizability of the findings and their contribution to educational policies. It can be recommended that sampling techniques in research be clearly specified and diversified. • • Research conducted with large and more diverse samples can be increased, alongside studies focusing on small and medium-scale samples. • The use of practice-based data collection instruments (such as lesson plans, diaries, and checklists) can be increased. • Studies encompassing in-service and pre-service primary school teacher groups from various regions can be encouraged to ensure regional research balance. 621 Bartın University Journal of Faculty of Education, 2026(2): Refereed Article • The current study examined Türkiye-based research; therefore, future research could investigate international literature to provide a comparative perspective on global and local AI trends.

  generalrecommendations
  Keywords: service based education school trends primary teachers conducted recommended various quantitative mixed comprehensive experiences applications
• Technological Evolution, Educational Transformation and the Promise of Artificial Intelligence (2026) · African Journal of Education and Practice · doi

  AI will make teaching easier AI will fasten content coverage AI will increase the demand for teachers AI is likely to make teachers lazy N Minimum Maximum Mean Std Deviation 315 315 315 3.50 3.57 2.12 1.51 1.42 1.44 1 1 1 5 5 5 315 1 5 3.75 1.43 Results in Table 4 above indicate that since the respondents scored 3.50 on the items “AI will make teaching easier” it implies that the respondents agreed with the statement. This finding agrees with Fitria’s (2021) finding in a study which explored how AI tools influence teaching and learning and found that teachers could save more energy and focus more on non-systematic work to create a more golden generation with good character and intelligent. This is in line with Allam et al’s (2023) argument that technology may assist instructors in reallocating some of their time to student learning by spending less time on repetitive tasks but take more time to interact with their students on a deeper level. Similarly, Bit et al., (2024) in their study found that AI systems can boost productivity, free up teacher’s time, and deliver more precise and consistent feedback. International Bank for Reconstruction and Development/The World Bank (2024) support this by arguing that AI technology has the potential to empower teachers to be more effective, efficient and responsive to the diverse needs of their students. This will eventually improve teaching efficiency (Seng, et al., 2025). Another theme that emerged from the interview is that AI will make teaching easy. This is exemplified by the following excerpt from one interviewee: “AI will make teaching easy because you can use it to make professional documents, set exams and get answers. This has made teaching simpler” (INT6). This is confirmed by questionnaire results on the item: AI will make teaching easier, which scored 3.50, implying that respondents agreed with the statement. The interview and questionnaire findings confirm the findings by Jaca (2024) and Mulyani, Istiaq, Shauki, Kurniati and Arlinda (2025) who argued that AI significantly enhances teaching performance by improving ease of use, usefulness and students’ learning outcomes. Another theme that emerged from the interview concerns laziness. One interviewee stated: “Teachers may not put in more effort if they discover that learners are using AI” (INT3). This was confirmed by the FGDs. In FGD2 one respondent stated: “Over-reliance on AI will make teachers lazy such that they cannot consult other materials and therefore students may not receive the right information”. The questionnaire results confirm interview and FGD results because the questionnaire respondents scored 3.75 on the item: AI is will make teachers lazy. Hence, questionnaire respondents agreed that AI will make teachers lazy. These findings ag

  generalfuture work
  Keywords: make teaching teachers respondents questionnaire lazy time students interview easier scored agreed learning statement finding
• Faculty Orientations Shape Adoption of AI in Research and Teaching (2026) · arXiv

  Future work should examine how such orientations develop over time, both in faculty and students, how they are shaped by disciplinary and institutional contexts, and how in- structional design can support productive integration of AI while addressing concerns about its impact on learn- ing. Although this study focuses on a population of peda- gogically engaged STEM faculty who may be more likely than the broader faculty population to experiment with emerging instructional technologies, even in this group there is no consensus regarding the pedagogical value of AI tools.

  generalinline gaps
  Keywords: faculty population future examine orientations develop time students shaped disciplinary institutional contexts structional design support
• Revista Académica Creatividad e Innovación en Educación (CIE Academic Journal) (2026) · Revista Académica Creatividad e Innovación en Educación · doi

  The study suggests that future research should focus on the development of new skills and competencies for educators to effectively integrate AI in their teaching practices. The paper also suggests that future research should explore the potential impact of AI on education in different contexts and countries. The study notes that there is a need for more research on the ethical implications of AI in education.

  generalfuture-work sectionevidence 5/5
  Keywords: study suggests future research focus development new skills
• Teachers’ Perceptions of Students’ Use of Artificial Intelligence (AI): A literature Review (2026) · JPAIR Institutional Research · doi

  Teachers express significant concerns about student over-reliance on AI, lack of critical engagement, and ethical misuse of AI outputs. The introduction of AI into the classroom alters the traditional roles of teachers, pushing them to adapt to new dynamics of instruction. There are concerns about unequal access to AI tools and the potential for exacerbating existing disparities in learning outcomes.

  generalstated challengesevidence 5/5
  Keywords: teachers express significant concerns about student over-reliance lack
• Creativity and writing with generative artificial intelligence in the master’s degree in teacher training (2025) · Frontiers in Education · cited 6× · doi

  The study identifies the challenge of introducing and integrating GenAI into established teaching activities. The research also highlights the need for teachers to develop pedagogical strategies for using AI in the classroom. The study notes the importance of addressing ethical considerations and the potential misuse of Artificial Intelligence, particularly in relation to intellectual property.

  generalstated challengesevidence 5/5
  Keywords: study identifies challenge introducing integrating genai established teaching
• Acceptance of Pre-Service Teachers Towards Artificial Intelligence (AI): The Role of AI-Related Teacher Training Courses and AI-TPACK Within the Technology Acceptance Model (2025) · Education Sciences · cited 45× · doi

  Current research offers limited insights into the role of factors regarding usage intentions and behaviors. The role of AI-related teacher training courses and AI-TPACK in pre-service teachers' AI acceptance is empirically underinvestigated. There is a need to investigate the relationships between pre-service teachers' participation in AI-related courses, their self-reported AI-TPACK, and their perceptions of AI's usefulness and ease of use.

  generalstated research gapevidence 5/5
  Keywords: current research offers limited insights role factors regarding
• Exploring the influence of anxiety and attitudes on pre-service teachers’ AI learning intentions: a structural equation modeling approach (2026) · Quality & Quantity · doi

  The study identifies the challenge of reducing anxiety associated with AI learning, which can impact learning motivations. The research highlights the need for teacher training programs to incorporate pedagogical content on AI to support learning motivation. The study notes the challenge of integrating AI technologies in education, which requires a comprehensive understanding of the dynamics between AI learning intentions, AI anxiety, and attitudes toward AI.

  generalstated challengesevidence 5/5
  Keywords: study identifies challenge reducing anxiety associated learning impact

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