Encouraging students to use AI and their own creativity in a balanced way

The gap

Evidence profile

Sourced from the recommendations and future work of the source papers, classified as general, spanning 8 journals. Those papers have been cited 1 times in total.

Supporting evidence — 8 representative gaps

• Students' Views on the Use of Artificial Intelligence in Language for Specific Purposes (LSP) Courses (2026) · International Journal of Instruction · doi

  The findings suggest a path emphasizing structure and responsibility in integrating artificial intelligence into language learning. A first step is the development of structured guidelines that can serve as a foundation for curriculum design. These guidelines should highlight pedagogical best practices and provide clear direction on navigating the ethical dimensions of AI in education. Equally important is the role of teachers, who remain central to the success of any technological innovation in the classroom. Comprehensive training programmes are needed to prepare educators with the technical skills to handle AI tools effectively and the critical awareness to address the ethical dilemmas that may arise. At the same time, promoting a balanced approach to AI integration is essential. While these technologies can enrich learning, students must also be guided toward maintaining their critical thinking and problem-solving abilities, ensuring that the human element of learning is not overshadowed by reliance on machines. AI has been demonstrated to increase competency and engagement, therefore educational institutions should encourage immersive, hands-on learning. It is essential to incorporate these chances into LSP courses in order to foster practical competency and well-informed opinions about AI's role in education. Institutions should also make ethical awareness a key part of the educational experience. By engaging students in discussions about the responsible use of AI, they can foster a culture of informed and reflective practice. Finally, experiential learning opportunities should be embedded within Language for Specific Purposes courses, allowing students to interact directly with AI tools. Such hands-on experiences can increase their confidence and competence, meaningfully bridging the gap between theory and practice. FINAL REMARKS the challenges of globalisation and Integrating AI into language education is an excellent opportunity to enhance learning outcomes while addressing technological advancement. Still, realising this potential requires a balanced approach that leverages the strengths of both traditional teaching methods and modern technologies. As Peng and Wan (2024) note, positive student perceptions are vital for successfully integrating innovative technologies into education. By addressing students' concerns and providing the necessary support, educators can create a more inclusive and effective learning environment that prepares learners for future challenges. In conclusion, this study contributes valuable insights concerning the evolving landscape of AI-assisted language learning while highlighting areas needing further research and policy development. As technology advances, ongoing

  generalrecommendations
  Keywords: learning language education students integrating ethical technologies development guidelines role technological educators tools critical awareness
• INVESTIGATING THE IMPACT OF ARTIFICIAL INTELLIGENCE ON CREATIVE WRITING SKILLS AMONG UNDERGRADUATE STUDENTS IN SIALKOT (2026) · Journal of Applied Linguistics and TESOL (JALT) · doi

  Encourage students to use AI and their own creativity in a balanced way. Students should use AI tools as drafting aids and create a final solution after brainstorming. Students should use AI tools to build their foundational skills, like grammar, vocabulary, and writing skills, to incorporate AI tools efficiently in their creative writing processes. Teachers should be trained to integrate AI tools ethically so they can guide students in their ethical use.

  generalrecommendations
  Keywords: students tools skills writing encourage creativity balanced drafting aids create final solution brainstorming build foundational
• Developing AI-Based Mathematics Learning Media to Foster Learning Independence in Early Childhood Education: An ADDIE-Based Study (2026) · European Journal of Educational Research · doi

  Based on the findings of this study, it is recommended that early childhood education institutions integrate AI-based learning media into mathematics instruction to foster children’s learning independence from an early age. The adaptive features of AI, such as immediate feedback and personalized learning challenges, have proven effective in supporting children’s understanding of basic mathematical concepts and should therefore be utilized as complementary tools alongside conventional teaching methods. Teachers are encouraged to receive continuous training and professional development related to the use of AI-based learning media. Adequate guidance will enable educators to optimally design learning activities, interpret data generated by AI systems, and provide appropriate pedagogical support that aligns with children’s developmental stages and individual learning needs. Future research is recommended to involve larger and more diverse samples across different early childhood education settings to strengthen the generalizability of the findings. Longitudinal studies are also suggested to examine the long- term impact of AI-based learning media on children’s learning independence, mathematical achievement, and other developmental aspects. In addition, policymakers and educational stakeholders should consider supporting the development and implementation of AI-based learning media through appropriate infrastructure, funding, and regulations. Such support will ensure that the integration of AI in early childhood education is sustainable, ethical, and accessible, ultimately improving the quality of learning in mathematics and other subjects.

  generalrecommendations
  Keywords: learning based early media children childhood education recommended mathematics independence supporting mathematical development appropriate support
• Assessment of Artificial Intelligence Awareness Level and Utilization Strategies Among Mathematics Students in Tertiary Institutions in Imo State (2026) · International Journal of Advanced Academic Research · doi

  1. Tertiary institutions in Imo State should organize regular workshops, seminars, and training programmes to improve students’ and lecturers’ knowledge of specialized AI mathematical tools such as Wolfram Alpha, GeoGebra, Symbolab, and Maple. 2. Educational authorities should integrate AI education into teacher-training curricula to ensure that pre-service teachers acquire practical skills in the use of AI technologies for teaching and learning mathematics. 3. Institutions should provide adequate ICT infrastructure, internet access, and digital learning facilities to support effective utilization of AI tools in mathematics education. 4. Mathematics lecturers should encourage students to adopt AI-driven personalized learning and intelligent tutoring systems to improve problem-solving abilities and academic performance. 5. Government and institutional administrators should develop policies and support systems that promote ethical and effective use of artificial intelligence in tertiary education. 71 International Journal of Advanced Academic Research | ISSN: 2488-9849 Vol. 12, Issue 5 (2026) | www.ijaar.org

  generalrecommendations
  Keywords: education learning mathematics tertiary institutions training improve students lecturers tools support effective systems academic state
• AI-Based Instructional Strategies on Student Engagement (2026) · International Journal For Multidisciplinary Research · doi

  Students are encouraged to use AI tools as learning supports for developing mathematical reasoning, not as replacements for thinking. They should verify AI-generated outputs with teacher guidance and focus on using AI to improve inquiry, reflection, and self-regulation skills. Mathematics teachers are encouraged to integrate AI in ways that promote metacognitive reflection and self-regulated learning through structured activities such as learning journals, think-aloud strategies, and AI-guided study planning. They are also encouraged to implement the proposed Policy Intervention Plan and use both classroom observation and AI usage data to better understand student engagement. The school administration is encouraged to adopt and pilot the Policy Intervention Plan, prioritize improvements in internet connectivity, and establish a school-based committee to oversee AI integration, address implementation challenges, and support continuous policy improvement. Parents and guardians are encouraged to support responsible and balanced AI use at home and, where possible, improve internet access. Participation in school AI orientation programs is also recommended to better support student learning. The Department of Education is encouraged to consider the study as evidence for AI integration in rural schools and to support wider implementation through infrastructure funding, device provision, and AI tool access, particularly in underserved areas. A broader AI policy framework grounded in rural school realities is also recommended. Future researchers are encouraged to replicate and expand the study in other rural contexts and to conduct longitudinal research on long-term academic outcomes, as well as studies on teacher training in AI pedagogy. The Policy Intervention Plan may also be adapted and tested in other educational settings.

  generalrecommendations
  Keywords: encouraged policy learning school support intervention plan rural teacher improve reflection self better student internet
• AI-Enhanced Sports Training in Physical Education: Global Research Trends, Pedagogical Methods, and Ethical Frameworks (2002–2025) (2026) · Physical Education Theory and Methodology · doi

  1. From “Technological Effectiveness” to “Teaching Effectiveness”: Grounded in curriculum objectives, principles of motor learning, and formative assessment, future work should develop interpretable AI and actionable feedback rules so that outputs can inform teachers’ instructional decisions rather than merely reporting model indicators (Abulibdeh et al., 2024). 2. Strengthen teacher education and ethical-audit capacity: Systematically incorporate AI literacy, bias identification, data governance, and safety-responsibility training into PETE, positioning teachers as key nodes in a human–machine collaborative loop to ensure classroom safety, assessment fairness, and instructional autonomy (Tedre et al., 2021). 3. Narrow the K–12 transition gap and resource disparities: Promote low-threshold, privacy-friendly deployment solutions and cross-school, cross-cultural validation standards; conduct replicable K–12 empirical studies and longitudinal follow-up to build a scalable evidence base for classroom implementation (Leal Filho et al., 2024). Conclusion, limitations and future avenues

  generalfuture work
  Keywords: effectiveness assessment future teachers instructional safety classroom cross technological teaching grounded curriculum objectives principles motor
• AI-Facilitated Self-Directed Learning and Mathematics Performance: A Mixed-Methods Study on ChatGPT Use among Generation Z Students (2026) · International Journal of Contemporary Educational Research · cited 1× · doi

  For mathematics teachers, it is recommended to explicitly teach AI-supported learning routines by establishing classroom norms such as requiring students to attempt problems first before consulting AI, explaining solutions in their own words, and verifying AI-generated answers using notes, teacher-provided examples, or alternative solution methods. Teachers should also strengthen students’ self-directed learning skills through structured scaffolds such as goal-setting templates, weekly learning plans, reflection logs, and self-check checklists aligned with key SRSSDL dimensions like awareness, learning strategies, and evaluation. In addition, classroom assessments and learning tasks should be designed to emphasize reasoning rather than copying by incorporating open-ended problems, error analysis activities, and solution-justification tasks that discourage simply pasting AI outputs. Finally, verification and evaluation should be taught as core mathematical habits by training students to check AI solutions for accuracy in units, logical steps, computations, and final answers, and to identify common reasoning errors that may occur in AI-generated explanations. For school leaders and program coordinators, developing school-based AI literacy guidelines is essential to promote responsible AI use, protect academic integrity, and ensure compliance with the Data Privacy Act of 2012 (RA 10173). Schools should also provide teacher capacity-building opportunities through training on AI-integrated lesson design, formative assessment strategies, and practical methods for guiding learners to use AI as a scaffold rather than a shortcut. Moreover, schools are encouraged to establish monitoring and support systems that allow teachers to track AI-related learning behaviors and provide targeted interventions, especially for students with low SDL readiness who may be more vulnerable to overreliance or passive use of AI tools. For students, ChatGPT should be used as a learning tutor rather than a solver by prioritizing requests for explanations, examples, and hints instead of complete final answers. Learners are encouraged to practice a “verify and reflect” habit by checking AI responses using notes or alternate methods and writing brief reflections on what they learned and how their understanding improved. To build independence gradually, students should reduce reliance over time by practicing similar problems without AI support after receiving initial guidance, ensuring that learning becomes internalized rather than tool-dependent. For future researchers, it is recommended to measure the quality of AI use rather than focusing only on frequency by including indicators such as verification behaviors, prompting strategies, and critical evaluation of AI outputs. Researchers may also test more explanatory models by examining mediators and moderators for example, whether SDL mediates the relationship between ChatGPT use and performance, or whether self-regulation moderates the benefits and risks of AI use. Longitudinal or experimental designs are encouraged to track changes over time and strengthen causal interpretation, and further studies should compare groups and contexts across grade levels, achievement levels, and access conditions like internet or device availability to explore equity implications and identify which learners benefit most from AI-supported learning.

  generalrecommendations
  Keywords: learning students rather teachers problems answers self strategies evaluation learners encouraged recommended supported classroom solutions
• Artificial Intelligence in Physics Education (2015–2025): Systematic Review of Trends, Applications, and Challenges (2026) · Journal of Education in Science Environment and Health · doi

  Based on the findings of this review, several recommendations can be proposed for the integration of generative AI in physics education. First, AI tools should be implemented under teacher guidance and within a clear pedagogical framework, rather than being provided directly to students. This approach ensures that AI serves as a supportive and complementary tool rather than a replacement for instructional guidance. Second, efforts should be made to enhance the reliability of AI in conceptual explanations, problem-solving, and feedback generation through model development and accuracy verification. Third, personalized and adaptive learning systems should be integrated into physics instruction to promote student motivation and improve learning outcomes. Fourth, both J Educ Sci Environ Health 195 teachers and students should receive education on ethical AI use, critical literacy, and responsible data practices to address potential risks related to misinformation and over-reliance. Fifth, future research should adopt long- term, discipline-specific experimental designs to evaluate the sustained impact of AI on learning and teaching in physics. Finally, hybrid AI systems that combine simulations, data-driven approaches, and language-based tools should be developed to support modeling, experimentation, and conceptual structuring, reflecting the core nature of physics education.

  generalrecommendations
  Keywords: physics education learning based tools guidance rather students conceptual systems review several recommendations proposed integration

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