The following are the recommendations based

The gap

Evidence profile

Stated in the recommendations and future work and limitations sections of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 7 journals. Those papers have been cited 135 times in total.

Supporting evidence — 8 representative gaps

• Exploring Teachers’ Lived Experiences in Assessing Authentic Student Learning in AI-influenced Classrooms: A Phenomenological Study (2026) · Journal of Education and Learning Reviews · doi

  In light of the findings of this study, the following recommendations are offered to support teachers, school leaders, and future researchers in responding to the challenges of assessing authentic student learning in AI-influenced classrooms. As teacher-researchers, these suggestions are grounded not only in the data but also in the shared realities of classroom practice. 1. Develop clear school policies on the responsible use of AI Given the increasing presence of artificial intelligence in education, it is essential for schools to establish clear, context-sensitive guidelines on how AI can be appropriately used in academic tasks. These policies should not simply prohibit AI use, but rather define its role as a support tool for learning rather than a substitute for thinking. Clear policies can help reduce confusion among students and teachers, promote academic integrity, and provide a consistent basis for assessment practices. 2. Strengthen authentic and process-based assessment practices Teachers are encouraged to move beyond reliance on polished written outputs and adopt assessment approaches that make student thinking more visible. These may include oral questioning, in-class written tasks, reflective responses, and performance-based assessments. By focusing on how students explain, apply, and engage with concepts, teachers can better ensure that learning is genuine and meaningful, even in AI-rich environments. 3. Integrate science-specific authentic assessment tasks Given the nature of science education, teachers may design tasks that require students to demonstrate understanding in real time and in context. These may include live laboratory demonstrations, oral defense of experimental results, real-time hypothesis formulation, data interpretation activities, and problem-solving tasks conducted under supervised conditions. Such approaches allow teachers to directly observe students’ reasoning processes and reduce overreliance on AI-generated outputs. leaders and educational 4.

  generalstated in recommendationsevidence 5/5
  Keywords: teachers tasks assessment students authentic learning clear policies support school leaders researchers student teacher given
• Teacher's Readiness On The Integration Of Artificial Intelligence In Teaching: A Basis For An Intervention Plan (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  The following are the recommendations based on the conclusion drawn: School Administrators should develop and implement a comprehensive AI policy that outlines ethical standards, responsible use guidelines, and data privacy protections. The HRDO should conduct continuous professional development, workshops, and certification courses on AI integration to enhance faculty competence particularly for senior faculty members who may require additional technical support. Faculty Members may design assessments that promote critical thinking and minimize excessive student dependency on AI and guide the students in verifying AI-generated content. School Administrators may adopt the intervention plan crafted as a basis in crafting the AI policy in the school Students should be oriented on ethical and responsible AI usage, including proper citation, disclosure of AI assistance, and critical evaluation of AI outputs. Future Researchers may widen the scope of the study since this is limited to faculty members who are teaching in the Tertiary level of and are encouraged to conduct a qualitative part of the study. the University, 1. 2. 3. 4. 5. 6.

  generalstated in recommendationsevidence 5/5
  Keywords: faculty school members administrators policy ethical responsible conduct critical students following recommendations based conclusion drawn
• Ensuring academic integrity in the age of ChatGPT: Rethinking exam design, assessment strategies, and ethical AI policies in higher education (2024) · Contemporary Educational Technology · cited 67× · doi

  Building on this study’s findings, several avenues for future research and practice are essential to further address the challenges posed by AI technologies in education. First, future studies should look beyond ChatGPT to see how other emerging AI tools and language models affect academic integrity. As new AI technologies with varying capabilities emerge, there is a need for comprehensive research that investigates their specific challenges and opportunities in a variety of educational contexts. Second, empirical research is needed to determine the effectiveness of the proposed exam redesigns and AI detection tools in real-world educational setups. Longitudinal studies that track the effects of innovative assessment strategies like project-based learning, oral exams, and real-time feedback mechanisms will provide helpful information about their impact on student learning, engagement, and academic integrity. Furthermore, field experiments to evaluate the accuracy and adaptability of AI detection software in detecting new types of AI-generated content are critical. Third, future research should investigate the creation and integration of more sophisticated AI detection tools that can keep up with AI models’ rapidly evolving capabilities. To ensure widespread adoption, these tools must be adaptable, detect nuanced AI-generated content, and seamlessly integrate with existing LMS. Fourth, the role of ethical frameworks in guiding the responsible use of AI in education demands more attention. Researchers should explore how HEIs can develop comprehensive, evidence-based policies that prevent AI misuse and promote AI technologies’ ethical and constructive use. This includes investigating best practices for training educators and students in AI literacy and responsible use. Finally, future research should look beyond higher education to see how AI affects academic integrity in other educational sectors like K-12, vocational training, and professional certification programs. Each sector may face unique challenges requiring customized solutions for assessment design, policy development, and AI tool integration. Future research can address these issues and help us better understand how to maintain academic integrity in an AI-driven educational landscape while maximizing the benefits of these technologies.

  generalstated in future workevidence 5/5
  Keywords: educational future technologies tools academic integrity challenges education detection generated ethical author address look beyond
• Ensuring academic integrity in the age of ChatGPT: Rethinking exam design, assessment strategies, and ethical AI policies in higher education (2024) · Contemporary Educational Technology · cited 67× · doi

  While this study provides an in-depth examination of the challenges and strategies for maintaining academic integrity in AI tools such as ChatGPT, several limitations should be noted. First, the study’s scope is limited primarily to ChatGPT, with little coverage of other emerging AI models and technologies that may pose similar or new challenges in higher education. However, the findings of this study, despite their limitations, can still provide valuable insights and strategies for maintaining academic integrity in AI tools and platforms. Second, the study is based primarily on existing literature, which means that real-world empirical data on the long-term effects of implementing the recommended exam design and AI detection strategies in diverse educational settings is urgently needed. This reliance on secondary sources restricts the ability to measure the practical impact of these approaches in live classroom environments, highlighting the importance of further research in this area. Third, the study concentrates on HEIs and does not account for potential differences in how AI tools impact academic integrity in other educational contexts, such as K-12 education or vocational training. The unique challenges and solutions in these other sectors may require further exploration. Finally, the rapid evolution of AI technology limits the study’s long-term relevance. As AI tools advance, new forms of academic misconduct may emerge, necessitating the evolution of detection tools. This study provides a snapshot of the current landscape, but further research will be required to stay ahead of these technological developments.

  generalstated in limitationsevidence 5/5
  Keywords: tools academic challenges strategies integrity further provides maintaining chatgpt limitations primarily education long term detection
• Academic Integrity and Students’ Ethical Use of ChatGPT in Higher Education (2026) · Journal of Information Technology Education Research · cited 1× · doi

  Keywords Conduct multi-institutional replications, experimental interventions on ethics/digital literacy training, and studies of assessment design that balance AI use with integrity (e.g., oral/ authentic assessments). artificial intelligence, academic integrity, ChatGPT, Gulf universities, PLS-SEM, student ethical responsibility, transparency, plagiarism avoidance, bias awareness, AI trust, digital literacy, AI usefulness, responsible use INTRODUCTION BACKGROUND The rapid integration of artificial intelligence tools such as ChatGPT in the education sector has attracted significant scholarly attention (Al-Jahwari & Yousif, 2025; X. Chen et al., 2020; Rejeb et al., 2024; Shishakly, 2025; Vieriu & Petrea, 2025; Zawacki-Richter et al., 2019). From the student perspective, prior research highlights the benefits of ChatGPT for writing, language learning, research, and administrative tasks (Dwivedi et al., 2023; Fitria, 2023; Lund & Wang, 2023; Shishakly et al., 2025). These systems provide real-time feedback on grammar, programming, and problem-solving by leveraging deep learning techniques to generate contextually relevant responses (Atlas, 2023; Baidoo- Anu & Owusu Ansah, 2023; Else, 2023; Herft, 2023; Kasneci et al., 2023; Qadir, 2022; Sallam, 2023; Sok & Heng, 2023; Susnjak, 2022; Vieriu & Petrea, 2025). Despite these advantages, scholars have raised substantial ethical concerns, including academic dishonesty, over reliance on AI, misinformation, and unfair assessment practices (Rudolph et al., 2023; Sok & Heng, 2023). Although ChatGPT can reduce instructional workload and foster pedagogical innovation (Cox, 2021), it also poses risks to academic integrity, responsible use, and algorithmic fairness (Farhi et al., 2023; Qadir, 2022; Welding, 2023). Ethical AI use is therefore expected to uphold fairness, transparency, privacy, and non-discrimination (Mhlanga, 2023). Plagiarism and contract cheating remain among the most pressing concerns (Cotton et al., 2024; Roe & Perkins, 2022), while transparency in disclosing AI assistance is increasingly emphasized as a foundation of academic credibility (Lamb, 2023; C. Lee & Cha, 2025; Tlili et al., 2023). RESEARCH GAP Although existing studies examine AI adoption and traditional academic misconduct, they offer limited empirical insight into how students conceptualize ethical responsibility when using generative AI tools. Most research focuses narrowly on plagiarism or cheating, with minimal attention to broader ethical dimensions such as transparency, responsible use, and algorithmic bias. Consequently, current academic integrity frameworks do not sufficiently account for AI-specific risks or student-level ethical decision-making. Furthermore, theoretical discussions often lack practical, evidence-based strategies to guide the ethical use of AI in educational contexts (Guerrero-Dib et al., 2020; Ramdani, 2018; Zawacki-Richter et al., 2019). Kumar et al.

  generalstated in future workevidence 5/5
  Keywords: academic ethical integrity chatgpt transparency student plagiarism responsible digital literacy assessment artificial intelligence responsibility bias
• Balancing Innovation and Integrity: Navigating the Challenges of Generative AI in Higher Education (2026) · International Journal of Advanced Corporate Learning (iJAC) · doi

  The risks of AI misuse still need to be addressed directly, rather than being dismissed as paranoia among educators and researchers. AI detection tools are one way to mitigate these risks, but they must be part of a broader strategy that includes education, policy development, and assessment reform. However, detection tools alone are insufficient, as they can mistakenly flag legitimate content. Therefore, we recommend that institutions not only invest in detection technologies but also train faculty to recognize the nuances of AI-generated content. Moreover, AI-resistant assessments should focus on real-world applications and critical thinking, areas where AI is less effective at substituting human input. Looking forward, we believe the future of higher education lies in how well we integrate AI into our teaching and research practices while maintaining our commitment to academic integrity. We should not be afraid of AI; instead, we should become proficient in its use and comfortable with allowing it to represent our voice when appropriate. The key is to remain vigilant about where AI assists and where it overreaches, ensuring that our own intellectual contributions stay at the forefront. By doing so, the entire academic community, comprising administrators, instructors, and students, can harness AI’s potential to enhance the educational experience while upholding the values of integrity, trust, and originality that are central to higher education. 9 DECLARATION OF GENERATIVE AI AND AI-ASSISTED TECHNOLOGIES IN THE WRITING PROCESS Statement: During the preparation of this work, the author(s), Norman S. St. Clair and Pamela D. McCrau, used ChatGPT 4.0 in order to improve the readability of the document. 60 International Journal of Advanced Corporate Learning (iJAC) iJAC | Vol. 19 No. 1 (2026) Balancing Innovation and Integrity: Navigating the Challenges of Generative AI in Higher Education After using this tool/service, the author(s) reviewed and edited the content as needed, and we take full responsibility for the content of the publication. 10 REFERENCES A. Bandura, “Social cognitive theory of self-regulation,” Organizational Behavior and Human Decision Processes, vol. 50, no. 2, pp. 248–287, 1991. https://doi.org/10.1016/ 0749-5978(91)90022-L D. R. E. Cotton, P. A. Cotton, and J. Shipway, “Chatting and cheating: Ensuring academic integrity in the era of ChatGPT,” Innovations in Education and Teaching International, vol. 61, no. 2, pp. 228–239, 2024. https://doi.org/10.1080/14703297.2023.2190148 S. Joshi, “Comprehensive review of AI hallucinations: Impacts and mitigation strategies for financial and business applications,” International Journal of Computer Applications Technology and Research, vol. 14, no. 6, pp. 38–50, 2025.

  generalstated in future workevidence 5/5
  Keywords: education content integrity detection applications higher academic international https risks tools technologies human teaching ensuring
• Adoption of AI-based proctoring platforms: A multi-stakeholder perspective from the education sector stakeholders (2026) · Journal of Technology and Science Education · doi

  The findings of the study offer valuable recommendations to support the responsible and effective implementation of AI-based proctoring systems. The purpose of these recommendations is to make professors, students, and parents responsive similarly, to ensure technology is shared fairly, and to support using digital assessments. Initially, it is essential to prioritize user-centric design principles to ensure accessibility and ease of use for wide range of users (Luo, 2024). AI-proctoring systems should incorporate intuitive interfaces, simplified and streamlined navigation, and clear, concise instructions to cater to both technologically adept users and those with limited digital proficiency (Somavarapu et al., 2024). Educational institutions should organize frequently orientation sessions, open Q&A (Questions and Answers) forums, and accessible documentation can develop user confidence and mitigate resistance. Moreover, the AI-based proctoring platforms should implement robust data privacy and protection protocols. To ensure ethical implementation, it is necessary to address fairness and algorithmic bias. This includes regular audits of AI algorithms, involvement of independent reviewers, and the establishment of redressal systems for users who perceive injustice in monitoring outcomes. Addressing these concerns is vital to maintain the credibility and integrity of assessment processes. At the policy level, adoption of AI-based proctoring systems should align with India’s Digital Personal Data Protection (DPDP) Act, 2023, ensuring lawful data processing, informed consent, purpose limitation, and adequate security safeguards. Institutions should establish internal AI governance frameworks consistent with national data protection regulations to enhance accountability, transparency, and stakeholder trust. To address the parameter on monitoring effectiveness, it is important to increase parental support (Moran et al., 2004). Providing evidence-based outcomes, such as reduced academic dishonesty and enhanced exam integrity. Furthermore, educational policymakers must embed ethical guidelines and accountability measures into the regulatory framework governing AI applications in education. These should cover transparency, data governance, fairness, and user consent, thereby creating a foundation for responsible innovation. Institutions should also establish mechanisms for ongoing feedback and iterative system improvement. This participatory approach, involving all stakeholders in system refinement, can enhance the responsiveness and effectiveness of AI-based solutions. By implementing the above recommendations, educational stakeholders can support a more equitable, trustworthy, and pedagogically aligned integration of AI-proctoring technologies. Kepping students in mind, since privacy concerns significantly influenced behavioural intention of students, institutions should implement transparent data-handling policies, provide clear consent mechanisms, and communicate how AI-based proctoring data are stored, processed, and deleted. For parents, as awareness and perceived usefulness are key factors, institutions should conduct orientation sessions and provide educational materials explaining system accuracy, fairness, and data protection safeguards. Finally, for faculty members training programs should be introduced to enhance trust in system reliability and ethical usage practices. Developers should incorporate clear explainability features into AI systems. For example, proctoring tools such as Proctorio and Respondus should provide instructors with accessible dashboards that clarify: what data are collected, how behavioral flags are generated, the probability thresholds for detecting “suspicious” activity and known limitations or bias risks. Providing interpretable AI outputs would reduce perceived ethical risk and increase trust among faculty.

  generalstated in recommendationsevidence 5/5
  Keywords: proctoring based systems institutions support educational protection ethical system recommendations students ensure digital user users
• Transforming Language Teaching With AI: An Investigation of Opportunities, Challenges, and Ethical Imperatives (2026) · Multidisciplinary Journal for Education Social and Technological Sciences · doi

  Several recommendations for research and practice can be developed based on these conclusions and limitations. Longitudinal studies should be designed to understand how educators and students integrate AI tools over time. By including data over a period of time, it will not only give a more comprehensive view of the immediate effects of the tools but will also take into account long-term uptake, resistance, and organizational change. More research will need to be completed on policy development based on the ethics of bias and established privacy concerns in the use of such tools, as well as issues relating to academic integrity. Understanding how organizations design and develop governance and accountability frameworks for responsible protest use is essential to the responsible use of AI in education. Qualitative approaches and methods, such as interviews and focus group studies, are also important in understanding the lived experiences of stakeholders, the local context of challenges, and the integration of AI into practice. From a practice perspective, organizations or institutions need to engage educators and systems in how to develop an AI literacy curriculum, so that educators can use AI tools responsibly and create structures to critically assess the outputs of AI content. Institutions must regularly audit AI tools and develop transparent processes for using AI in learning spaces in order to see risk assessment both in time and function in reducing bias and misuse, as well as reducing overreliance on these systems. Policymakers must recognize that the sustainability of these systems is not only about how technically efficient these educational technologies are, but also about how they can adapt and continue to evolve, in keeping with ethical standards, inclusivity, and academic integrity. A comprehensive strategy that integrates these dimensions is essential to ensure that AI adoption enhances rather than undermines the foundations of higher education.

  generalstated in recommendationsevidence 5/5
  Keywords: tools author practice educators time develop systems based understand comprehensive need bias well academic integrity

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