AI in – Universities and online educators are shifting from bans to smarter course design, emphasizing process-based assessment, AI literacy, and collaboration.
AI is no longer a side issue in higher education—it has quietly moved into everyday student work, forcing online learning to rethink what it values.
A 2025 survey by the Higher Education Policy Institute found that 92% of university students now use AI tools in their studies. up from 66% the previous year.. For online educators. that change flips the conversation from whether AI should be allowed to how courses can be designed so students are still building deep understanding. regardless of whether a chatbot is involved.
That shift also comes with a difficult reality: both traditional grading practices and AI-detection software struggle to reliably sort out how work was produced.. In research discussed by Kofinas. Tsay. and Pike (2025). a study across two UK universities reported that experienced markers had trouble reliably distinguishing student writing from content generated or altered by ChatGPT. with detection rates as low as 33%.. At the same time, Liang et al.. (2023) found that seven widely used AI detectors misclassified more than 61% of essays by non-native English speakers as AI-generated.. The combined message for online assessment is clear: if neither human judgment nor detectors can consistently identify AI use. educators need to focus less on policing tools and more on uncovering learning progress.
One practical response is to assess learning as it develops, not only at the end.. In online courses, the approach described relies on staged assignments with checkpoint milestones.. Students begin by submitting a topic proposal that includes their personal rationale. then move to an annotated outline with initial sources.. After that, they submit a rough draft together with a reflective memo explaining their reasoning and the challenges they encountered.. The final submission closes with a revision narrative that documents how their thinking evolved.. This documentation is meant to create a traceable record of intellectual development that is far harder to fabricate through AI.
Video-based explanation is another method highlighted for environments where instructors cannot rely on in-person cues.. Instead of only submitting a final paper or file. students are asked to record a walkthrough explaining how they approached the project. the choices they made. and how they addressed specific challenges.. Grading the explanation alongside the deliverable is presented as a way to gain a clearer view of what students truly understand—especially in asynchronous online settings where comprehension is otherwise harder to gauge.
Rather than banning AI. the guidance encourages educators to design assignments that require students to use AI tools and then evaluate the results critically.. In a programming course example. students may use AI to explain existing code or to propose solutions to bugs. but they are then expected to test each suggestion and explain—through comments—why a specific fix works or fails.. Students also reflect briefly on what they learned about debugging strategies.. The evaluation centers on the testing process and the reasoning behind the conclusions, not simply the AI’s initial suggestions.. The underlying goal is to push students past copying and toward engagement with problem-solving.
To address lower interaction in asynchronous courses, the approach also includes a new way to structure weekly discussions.. Students use AI tools to generate practice questions and sample answers. then post those as original discussion contributions that include their own reflections.. They are asked to identify which questions were most helpful and to note any gaps in the tool’s knowledge.. Students also evaluate at least two other question-and-answer sets created by peers.. This shifts discussion away from the instructor being the sole quiz-maker and toward a peer dialogue centered on testing understanding and spotting limitations.
Online learning often comes with a downside: isolation.. The report argues that AI can worsen that risk if students use chatbots as a replacement for peer engagement rather than a supplement to learning.. Research cited in the discussion (Long et al.. 2026) suggests that AI tools work best when paired with interactive teaching strategies like project-based learning and scaffolded feedback. emphasizing that the technology should complement human interaction rather than replace it.
Collaborative learning is presented as a key lever for strengthening engagement.. Drawing on the widely cited work of Johnson. Johnson. and Smith (2007). the idea is that students learn more effectively when they work together toward shared academic goals.. In practice, the guidance recommends activities that naturally make AI shortcuts less effective.. Jigsaw assignments. for example. assign each student a component of a topic to master and then teach to classmates. after which the group synthesizes the full picture.. Collaborative peer programming projects are described as another option. where teams split roles. establish working agreements. and build the project collectively—so accountability is embedded in each member’s contribution.. Structured peer review tasks also aim to keep students active by prompting them to evaluate a classmate’s work and provide constructive feedback before the final submission.
Making AI literacy part of the curriculum is also treated as essential if students are expected to use these tools responsibly.. The guidance recommends adding AI awareness modules that clarify appropriate versus inappropriate uses. including distinctions such as seeking hints compared with copying complete solutions.. This framing can be adapted to the early modules of a broad range of online courses.
One concrete practice offered is “AI transparency training” early in the term.. Students experiment with generative AI tools such as ChatGPT through low-stakes assignments and then reflect on the AI’s accuracy. its mistakes. and what it cannot replicate about their own contributions.. The purpose is to replace a culture of secrecy with transparency. setting expectations for how AI will be used throughout the semester.
At the center of the recommendations is a principle about what educators should prioritize: learning itself rather than the final output.. While AI can make polished work easier to produce. the argument is that it does not replace what instructors need to assess uniquely—reasoning. collaboration. and the ability to explain understanding.. As Kofinas. Tsay. and Pike (2025) note. generative AI increases the importance of social learning because explicit knowledge can be reproduced quickly by machines.. That makes it even more crucial to observe how students apply, question, and communicate their thinking.
The broader implication for online education is that the classroom does not have to become a place where AI diminishes learning.. Instead. with deliberate course design. AI can be integrated in a way that pushes students toward deeper engagement and better alignment with real-world preparation.. The shift is not presented as effortless. but as a way to transform online learning into a more challenging experience that still supports student development—rather than relying on rigid bans or unreliable detection.
AI in online courses higher education policy AI assessment AI literacy collaborative learning online teaching strategies