A survey conducted in 2025 by the Higher Education Policy Institute revealed that 92% of university students currently utilize AI tools in their studies, an increase from 66% the year before (Freeman 2025). For educators in online settings, the focus has shifted from whether to permit AI to how to design courses that promote in-depth learning, regardless of AI’s involvement. The following strategies, grounded in existing research, personal teaching experience, and evolving best practices, present practical methods to integrate AI into online courses while emphasizing genuine learning.<\/p>\n
When evaluation relies solely on final essays, reports, or projects, there is a risk of AI completing most of the work undetected. Kofinas, Tsay, and Pike (2025) pointed out this challenge: in a study at two UK universities, experienced markers often failed to distinguish between student work and content generated or altered by ChatGPT, with detection rates as low as 33%. AI detection tools are similarly flawed; Liang et al. (2023) found that seven popular detectors incorrectly labeled over 61% of essays by non-native English speakers as AI-generated. Given that both human judgment and automated detection struggle to reliably identify AI use, new assessment methods are needed to track learning progress throughout the process.<\/p>\n
In many of my online courses, I utilize staged assignments with required checkpoints. Initially, students submit a topic proposal explaining their personal rationale, followed by an annotated outline with preliminary sources. They then provide a rough draft with a reflective memo detailing their reasoning and any challenges faced. The process concludes with a final submission that includes a revision narrative. These stages create a documented record of intellectual development that AI cannot easily fabricate. Additionally, students record videos of their final projects, offering a walkthrough of their process, decision-making, and problem-solving strategies. Grading this explanation alongside their deliverable offers a clearer understanding of their grasp of the material.<\/p>\n
Rather than banning AI, assigning tasks that require students to utilize AI tools and then critically assess the outputs is more effective. For instance, in my programming course, students might use AI to interpret existing code or propose bug fixes. They are expected to test each suggestion, explain in comments why a fix works or doesn’t, and reflect on their learning about debugging strategies. My evaluation focuses on their testing process and reasoning rather than the AI’s initial suggestions.<\/p>\n
In asynchronous online courses with limited instructor interaction, I have implemented a new approach to boost engagement in weekly discussions. I ask students to use AI tools to generate practice questions and answers, allowing for self-assessment. They then post these AI-generated questions and answers as original discussion posts, reflecting on which were most helpful and identifying any knowledge gaps. Additionally, they evaluate at least two other sets created by peers, fostering a dialogue centered on critical assessment and reducing the instructor’s workload in creating quizzes.<\/p>\n
Online courses can lead to feelings of isolation, and AI use may exacerbate this if students rely solely on chatbots instead of engaging with peers. Research suggests that AI tools are most effective when part of interactive teaching strategies like project-based learning and scaffolded feedback (Long et al. 2026). The technology excels when it complements human interaction rather than replaces it. Collaborative learning, where students work together toward shared academic goals, is among the most effective strategies for enhancing online engagement (Johnson, Johnson, and Smith 2007). In my courses, I include various collaborative activities that naturally discourage AI shortcuts.<\/p>\n
If students are to effectively use AI, it is essential to teach them responsible usage. Incorporating AI awareness modules into classes can provide students with clear guidelines for appropriate and inappropriate uses, such as seeking hints versus copying complete solutions. This framework can be integrated into the introductory modules of any online course. I dedicate one early class session each semester to “AI transparency training,” where students experiment with Generative AI tools like ChatGPT on low-stakes assignments and reflect on the AI’s accuracy, errors, and their own contributions that AI couldn’t replicate.<\/p>\n
The core principle of these strategies is clear: emphasize the learning process over the final product. While AI has simplified creating polished outputs, it hasn’t replaced the ability to evaluate reasoning, collaboration, and explanation. As Kofinas, Tsay, and Pike (2025) noted, generative AI has made social learning more critical since explicit knowledge can now be quickly reproduced by machines. What remains uniquely human, and therefore fundamentally valuable to assess, is how students apply, question, and communicate their understanding.<\/p>\n
The online classroom doesn’t need to be a setting where AI diminishes learning. With intentional design, it can become a space where AI supports and enhances the learning process. Although this shift requires effort, it leads to a more challenging learning experience aligned with the real world students are preparing to enter. Taoufik Ennoure, PhD, a seasoned Computer Science Educator and Researcher, is an Associate Professor at the Community College of Philadelphia and teaches at NYIT, Monroe University, and Baruch College, City University of New York. His research interests include Algorithm Optimization, Artificial Intelligence, Big Data Analytics, and online learning pedagogy.<\/p>\n