OECD Digital Education Outlook 2026: Exploring Effective Uses of Generative AI in Education

JeepyTA is also capable of responding to student reflections and questions on the course readings and lectures, offering additional clarification, prompting further thinking, and connecting ideas across course materials. When students share reflections, JeepyTA acknowledges their contributions by reinforcing key ideas from the readings or connecting their insights to broader course themes. If a student raises a question about a concept, theory, or method, JeepyTA provides a response by summarising relevant arguments, explaining terms, or pointing to sections of the readings that address the issue (see Figure 9.3). When a reflection introduces an interesting perspective or critique, JeepyTA may pose follow-up questions to encourage further discussion. To maintain consistency between JeepyTA’s responses and the course content, JeepyTA was specifically instructed to first reference the course materials, with specific materials selected based on their similarity score to the student’s query, while replying. JeepyTA was also prompted to use course-specific language as defined by instructors before the start of the semester in its responses. If a student’s question is not closely related to the course, JeepyTA may be instructed to rely on its knowledge base to respond.

Instructors or TAs can modify the visibility settings of JeepyTA’s responses at any time during the semester. If preferred, JeepyTA’s responses can be flagged for instructor review before being shared with the students. This option can be turned on or offat any time during implementation. It is also available for selected categories, such as only for answering logistics questions. This helps prevent the provision of incorrect or misleading information, a particular issue in subject areas where misconceptions are highly present on the web and therefore also in the LLM knowledge base. Instructors or TAs can review flagged responses. If a response is inaccurate, they can discard it and reply directly. If the response is mostly correct but needs refinement, they can edit it before posting. When a response is accurate and well-structured, the instructors or TAs can approve it as is. This additional layer allows JeepyTA to provide timely support while making sure students receive information that is accurate, relevant, and aligned with the course objectives.

Provide debugging support to programming code

For courses involving programming (but not focused on learning to programme), JeepyTA also provides debugging support to students working on assignments by analysing their code and identifying potential errors. When students submit code snippets or describe issues they encounter, JeepyTA reviews the logic, syntax, and structure to pinpoint common mistakes. It then suggests corrections or improvements to resolve the errors (see Figure 9.4).

JeepyTA is generally encouraged to use its existing programming knowledge to solve coding issues while following course-specific conventions or practices. In an Educational Data Mining course, for example, student-level cross- validation is required because this method evaluates how well a model generalises to unseen students. A general LLM chatbot may default to recommending a simple train-test split, a technique that would be acceptable in other contexts but is not the method needed in this course. To prevent this, JeepyTA is instructed to prioritise debugging support based on course materials, assignment requirements, and instructor guidelines rather than relying on broadly used techniques that may not be appropriate in the course context. When addressing programming errors, it refers to the course’s preferred approaches and explains why they are used and how they differ from other methods.

In cases where code produces unexpected output, JeepyTA offers strategies for troubleshooting, such as adding print statements, checking variable values, or breaking down complex functions into smaller, testable parts. If students describe the problem rather than submitting code, JeepyTA suggests debugging techniques based on the nature of the issue and guides them through potential causes and solutions.

If students describe the problem vaguely or provide incomplete context, JeepyTA asks follow-up questions to clarify the issue before offering suggestions. For example, if a student says, “The code isn’t working,” without specifying the error message or expected output, JeepyTA prompts them to provide more details, such as the error message received, the intended function of the code, or the steps they have already tried.

When providing programming code support, JeepyTA’s prompts are designed to avoid simply providing the correct code but instead focus on helping students understand how to diagnose and fix errors themselves. The prompt design encourages students to learn from their own debugging process, which creates opportunities for them to build confidence and capacity to read errors, trace code, and solve problems independently rather than rely on being given the exact fix.

JeepyTA has provided debugging support for two courses across two semesters, but its effectiveness in identifying errors has been limited in some cases. One possible reason is that it did not have access to the datasets students were working on in either implementation, which makes it difficult for JeepyTA to verify data structures, variable values, or dataset-specific errors. On the other hand, in other cases, it has caught unusual mistakes (such as a student using the symbol \ instead of |) and typos which can be difficult for instructors and human TAs to see in a lengthy programme (see Figure 9.4). Even when JeepyTA is unable to pinpoint the exact cause of issues, it has helped students clarify their problems and suggests general debugging strategies. This still reduces the time instructors or TAs need to spend guiding students through the initial steps of troubleshooting.

Summarise the discussion posts for each week

JeepyTA has also been applied to generate summaries of discussion forum conversations. When the use case was first introduced in Spring 2024, summaries were provided only to instructors and TAs to give them an overview of the student discussions. As of Spring 2025, in some courses, these summaries are accessible to all students on the forum. After each weekly discussion, JeepyTA summarises key themes, groups discussions into meaningful themes and recurring arguments, and identifies important questions from the discussion forum. In doing so, JeepyTA credits students who introduced specific points in order to give the instructor and TAs a sense of specific student participation as well as overall trends (see Figure 9.5).

The purpose of these summaries is not to replace reading or participating in discussions but to provide an additional layer of support in organising and reflecting on what was discussed. Students are still expected to engage in the full conversation, but the summary can help identify patterns, highlight areas of agreement and disagreement, and uncover questions that might require further discussion. In other words, instead of replacing direct engagement, the summary function can serve as a tool to make the overall direction of conversations more accessible.

Suggest ideas for essays and support brainstorming

In a Games and Learning course offered in Spring 2024, JeepyTA was used as a brainstorming partner in two play journal assignments, where students consulted with JeepyTA to propose educational uses for classic games and Minecraft (Shah et al., 2024). A play journal is a structured reflective assignment in which students document and analyse their gameplay experiences to critically engage with classic and contemporary video games by examining their design, narrative, and educational potential. These journals help students develop knowledge of a game through both direct and vicarious experiences, providing a baseline understanding that enables them to propose meaningful educational applications. This structured reflection also prepares students with foundational knowledge of the game, which they can draw upon when consulting with JeepyTA – thus streamlining the labour-intensive process of game analysis and educational integration. While recent initiatives have aimed to make the repurposing of commercial entertainment games for education more accessible (Foster and Shah, 2020), the process still requires significant human effort. This poses challenges for students new to game-based learning and for instructors with limited resources to support student experimentation through direct experience (e.g. playing the game, learning through trial and error) or vicarious experience (e.g. watching YouTube videos, learning from colleagues). JeepyTA offers a solution by enabling users with varying levels of technological, pedagogical, and content knowledge to generate ideas, helping them explore how a game can be adapted for specific educational contexts and learning goals. Students can be guided in developing familiarity with a game and using it in dialogue with JeepyTA (see Figure 9.6).