Political Science Educator: Volume 30, Issue 1
Reflections
By Niva Golan-Nadir (niva.golan@post.runi.ac.il)
Responses to the challenges posed by artificial intelligence (AI) in academia are rapidly evolving and taking diverse forms. Recent literature highlights growing concern in higher education regarding generative AI tools, such as ChatGPT, Bing, and Microsoft Copilot, particularly in relation to academic integrity (Bittle & El-Gayar, 2025). A central concern is that students may rely on these tools to cheat or plagiarize written assignments and exams (Akintande 2024; Chan 2023). In response, universities have begun to develop institutional policies that guide students on appropriate AI usage (Dabis & Csáki 2024; Humble 2025; Spivakovsky et al. 2023; Oh & Sanfilippo 2025).
AI policies operate at the institutional and the instructional levels. While institutions provide general guidelines, individual lecturers retain significant discretion in adapting these policies to their specific courses, which vary in assessments, content, learning outcomes, and modality. These course-specific guidelines are typically articulated in the syllabus.
Institutional alternatives – Three Types of Artificial Intelligence Tool Usage Policy
Recent literature on artificial intelligence integration in higher education highlights that institutions adopt diverse strategies, ranging from embracing AI tools to enhance teaching efficiency and student engagement to addressing ethical concerns such as academic integrity and data privacy (McCusker & Michalak, 2025). At the same time, it shows that in most universities, faculty retain considerable discretion in determining how these tools are used in their courses – a framework that may encourage innovation and creativity in AI implementation (Alqahtani & Wafula, 2025; Wang et al., 2024). The approach at Reichman University aligns with this perspective.
University guidelines typically require each syllabus to clearly state the course’s AI policy. In most cases, AI use falls into one of three categories: (1) permitted, (2) permitted under specific conditions, or (3) prohibited. A clearly articulated AI policy serves both as a preventive measure and as an educational tool. It reduces ambiguity, clarifies expectations, and minimizes potential violations of academic integrity. Moreover, it helps align assessment methods with learning objectives and enables pedagogical innovation. Drawing on the policy implemented at Reichman University, where I teach, the distinctions between these categories are clearly defined, along with the respective advantages and risks of each option:
- Permitted: Students may use AI tools extensively, provided that such use is fully disclosed and appropriately verified. This approach reflects the realities of contemporary professional environments, supports the development of essential AI literacy skills, and can streamline learning and research processes. At the same time, it carries notable risks, including over-reliance on AI, the possibility of hidden inaccuracies, and the erosion of foundational skills. It may also raise concerns regarding student data privacy. Accordingly, students are typically required to submit a declaration of AI usage, a detailed appendix explaining how the tools were used, and evidence of verification procedures.
- Permitted Under Conditions: AI tools may be used only for specific tasks and under clearly defined circumstances. This approach allows for targeted pedagogical support while maintaining academic integrity. However, it may create ambiguous boundaries of acceptable use and can increase the burden of monitoring and enforcement for instructors. In such cases, assignments must be explicitly designated as allowing AI use, accompanied by brief explanatory notes from students on how the tools were employed, as well as ongoing monitoring checks.
- Prohibited: The use of AI tools is strictly forbidden for all course assignments. This ensures the originality of student work and enables a more accurate assessment of students’ independent mastery of skills. However, it also limits opportunities for students to develop competencies relevant to the evolving job market and may encourage covert, undisclosed usage. To mitigate these risks, students must submit a declaration confirming non-use of AI, provide drafts and evidence of their work process, and, where necessary, undergo verification through oral examination.
Lecturer’s discretion
Institutional frameworks leave considerable room for instructor-level decision-making. From a pedagogical perspective, lecturers must align AI policies with course objectives, content, and student needs. In my own teaching, specifically in an undergraduate Comparative Politics course, I adopt the “Permitted Under Conditions” approach. This choice reflects the core goals of the course: introducing foundational theories and analytical skills among undergraduate students. The course introduces multiple theoretical frameworks alongside the comparative method, equipping students with the tools needed to analyze case studies and contemporary political events. Allowing unrestricted AI use in analytical assignments risks undermining these objectives.
At the same time, my teaching context introduces additional considerations. I teach at an international school with students from over 90 countries, many of whom are non-native English speakers. To support them, I allow limited use of AI for language editing purposes only. This approach enhances clarity and accessibility while maintaining analytical rigor.
Incorporating AI Under Controlled Conditions
Despite a generally limiting policy, I incorporate structured and limited AI engagement through specific assignments. Two assignments I have used recently include the ‘student vs. AI’ news article analysis and a creative ‘TikTok Reflection’ Assignment.
Comparative Analysis with AI (Midterm Assignment)
In a midterm assignment on democratic theory, students analyze a contemporary news article using course concepts. They are then required to compare their analysis with that generated by an AI tool (e.g., ChatGPT).
Assignment components include:
- Selecting a recent course related news article from a reliable source
- Applying three course concepts/theories to analyze the article
- Prompting AI to identify relevant concepts/theories and justify its choices
- Critically comparing the AI-generated analysis with their own and explaining who they think had a “better” explanation and analysis.
This exercise encourages reflection on both the strengths and limitations of AI-generated interpretations. The assignment is designed to foster media literacy by developing students’ critical thinking skills, enabling them to critically assess information encountered through mass communication channels and to form independent judgments about media content (Potter, 2010; Silverblatt & Eliceiri, 1997). To this end, students are required to produce two forms of analysis and compare them, thereby practicing how to access, analyze, evaluate, and communicate insights about a media article in different ways (Hobbs, 2004), while also engaging constructively with AI-generated analysis.
In this midterm assignment, a clear divergence emerged between stronger and less-prepared students. Stronger students closely followed the instructions, producing independent analyses and engaging critically with the AI-generated responses; many even expressed enthusiasm in evaluating whether their own reasoning “outperformed” the AI. By contrast, some less-prepared students appeared to rely on AI for both parts of the task, subsequently claiming that “we thought the same way.” While perhaps unintended, this approach effectively bypassed the comparative component of the assignment. As a result, it undermined the core pedagogical objective of encouraging independent analytical thinking and critical reflection on the strengths and limitations of AI-generated interpretations.2.
TikTok Reflection Assignment
Students create a short-form video (up to 90 seconds) that applies course concepts to real-world cases. They may choose from formats such as:
- Concept/Theory in Action – Using a case study example to show how a concept is playing out in the real world
- Theory Meets Current Events – Showing how a concept helps us to better understand a real-world event
- Educational skits – Presenting a theoretical concept or argument through a short skit that is humorous and engaging
In this assignment, students may use AI tools creatively, for example, to animate content, which is highly useful in cases where they feel shy to appear in the video vocally or physically, or enhance presentation. The emphasis is on critical engagement, creativity, and accessibility. Students are encouraged to “be creative, be thoughtful, and, most importantly, have fun while demonstrating critical thinking and course engagement.” Social media and collaboration technologies are widely viewed as valuable tools for fostering a more collaborative learning environment, particularly in higher education (Al-Rahmi et al., 2014; Okoro, 2012). Recent scholarship highlights the potential of the Internet and social media to enhance the effectiveness and productivity of teaching and learning processes. Incorporating social media assignments as part of innovative online teaching and assessment practices has generally been met with positive responses, as such approaches can help humanize the course experience (Stewart, 2023).
The TikTok assignment provided students with a high degree of creative freedom, allowing them to express their ideas in a variety of formats, including the use of AI tools. As a result, the submissions demonstrated a wide range of innovative approaches to integrating AI into their work. Many students used AI-generated voiceovers to deliver their scripts, creating a more polished and professional flow. Others relied on AI-generated visuals, producing entire videos composed of graphics and scenes created through AI platforms. Some incorporated recognizable settings, such as well-known podcast studios, and used AI to insert themselves into these environments, effectively presenting their content as if they were speaking within those spaces. Additionally, students used animation tools to create AI-generated versions of themselves, further enhancing the visual and narrative quality of their videos. These approaches reflect a growing level of digital literacy among students, particularly in their ability to integrate AI tools not only for efficiency but also for enhancing storytelling and audience engagement. Overall, the assignment highlighted not only students’ creativity but also their ability to engage with emerging technologies in thoughtful and diverse ways.
Conclusion
As AI becomes increasingly embedded in academic life, avoiding it entirely is neither feasible nor desirable. A constructive institutional framework for appropriate use, combined with course-specific and guided integration, can support skill development, foster innovation, and maintain the relevance of academic training. At the same time, policies must remain sensitive to course-specific goals and pedagogical priorities. However, even with restrictive policies, AI usage is difficult to fully control. In smaller classes, instructors may more easily identify deviations in students’ writing styles, whereas in large courses, misuse is typically detected only when it is particularly evident.
The rapid transformations facing academia since the COVID-19 pandemic have underscored the need for institutional adaptability. AI is now part of this evolving landscape, and thoughtful policy design, combined with instructor discretion, remains essential. Otherwise, maintaining academic integrity may increasingly depend on a return to in-class assessments, such as handwritten assignments.
References
Akintande, Olalekan J. 2024. Artificial versus natural intelligence: Overcoming students’ cheating likelihood with artificial intelligence tools during virtual assessment. Future in Educational Research, 2(2), 147-165. https://doi.org/10.1002/fer3.33
Alqahtani, Naifa, and Zarina Wafula. 2025. Artificial intelligence integration: Pedagogical strategies and policies at leading universities. Innovative Higher Education, 50(2), 665-684. https://doi.org/10.1007/s10755-024-09749-x
Al-Rahmi, Waleed Mugahed, Mohd Shahizan Othman, and Mahdi Alhaji Musa. 2014. The improvement of students’ academic performance by using social media through collaborative learning in Malaysian higher education. Asian social science, 10(8), 210. doi:10.5539/ass.v10n8p210
Bittle, Kyle, and Omar El-Gayar. 2025. Generative AI and academic integrity in higher education: A systematic review and research agenda. Information, 16(4), 296. https://doi.org/10.3390/info16040296
Chan, Cecilia Ka Yuk. 2023. A comprehensive AI policy education framework for university teaching and learning. International journal of educational technology in higher education, 20(1), 38. https://doi.org/10.1186/s41239-023-00408-3
Dabis, Attila, and Csaba Csáki. 2024. AI and ethics: Investigating the first policy responses of higher education institutions to the challenge of generative AI. Humanities and Social Sciences Communications, 11(1), 1-13. https://doi.org/10.1057/s41599-024-03526-z
Hobbs, Renee. 2004. A review of school-based initiatives in media literacy education. American Behavioral Scientist, 48(1), 42-59. https://doi.org/10.1177/0002764204267250 Humble, Niklas. 2025. Higher education AI policies—A document analysis of university guidelines. European Journal of Education, 60(3), e70214. https://doi.org/10.1111/ejed.70214
McCusker, Erin, and Russell Michalak. 2025. AI Policies in US Universities: A Critical Analysis of Policy Gaps and Library Involvement. Journal of Library Administration, 65(6-7), 808-824. https://doi.org/10.1080/01930826.2025.2560268
Oh, Sang Hoo, and Madelyn Rose Sanfilippo. 2025. Responsible AI in academia: policies and guidelines in US universities. Information and Learning Sciences, 126(9-10), 561-587. https://doi.org/10.1108/ILS-03-2025-0042
Okoro, Ephraim. 2012. Integrating social media technologies in higher education: Costs-benefits analysis. Journal of International Education Research, 8(3), 255.
Potter, W. James. 2010. The state of media literacy. Journal of broadcasting & electronic media, 54(4), 675-696. https://doi.org/10.1111/soc4.12041
Stewart, Olivia G. 2023. Understanding What Works in Humanizing Higher Education Online Courses: Connecting through Videos, Feedback, Multimodal Assignments, and Social Media. Issues and Trends in Learning Technologies, 11(2), 2-26.
Wang, Hui, et al. 2024. Generative AI in higher education: Seeing ChatGPT through universities’ policies, resources, and guidelines. Computers and Education: Artificial Intelligence, 7, 100326. https://doi.org/10.1016/j.caeai.2024.100326
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Niva Golan-Nadir is a Research Associate at the Center for Policy Research, Rockefeller College of Public Affairs & Policy at the University at Albany, SUNY, and at the Institute for Liberty and Responsibility at the Lauder School of Government, Diplomacy & Strategy, Reichman University, where she teaches Comparative Politics and Research Methods. She is also a member of the research group “The Ongoing State of the Field of the Scholarship of Teaching and Learning in Political Science” at the APSA Annual Virtual Research Meeting.
Published since 2005, The Political Science Educator is the newsletter of the Political Science Education Section of the American Political Science Association. As part of APSA’s mission to support political science education across the discipline, APSA Educate has republished The Political Science Educator since 2021. Please visit APSA Educate’s Political Science Educator digital collection.
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