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

JeepyTA is an example of a course-specific, AI-driven Teaching Assistant designed to integrate with existing classroom and online practices. Developed by the Penn Center for Learning Analytics at the University of Pennsylvania (UPenn) and launched in Fall 2023 (Liu et al., forthcoming), JeepyTA utilises a multi-turn conversational architecture of large language models (LLMs) and is not bound to a specific model – it can be configured to run on many LLMs (e.g. GPT, Llama, or DeepSeek). In courses where JeepyTA has been used, a recent OpenAI GPT model has been used (starting with GPT-3.5 Turbo, moving to GPT-4, GPT-4-Turbo and GPT-4o).

JeepyTA has been used in various scenarios: to deliver responses to logistics questions, contextually respond in discussions based on provided course materials, provide targeted feedback to written assignments and coding problems, and to serve as a brainstorming partner. As of Spring 2025, JeepyTA has been deployed across 16 sessions of 14 courses at three higher education institutions in the USA (with deployments also running later in 2025 in Singapore and Colombia). This widespread adoption reflects the growing interest in AI systems that can free human instructors and TAs from repetitive logistical duties, while still delivering responsive, round-the-clock support for learners.

To ensure course alignment, JeepyTA is primed with instructors’ chosen reference materials, including syllabi, textbooks, readings, and past instructor feedback. These resources are embedded in the system’s knowledge base through a retrieval-augmented generation (RAG) workflow: newly uploaded documents are converted into embedding vectors, enabling JeepyTA to retrieve contextually relevant information via semantic search. In doing so, JeepyTA can address administrative queries – such as answering date-related questions from the syllabus – while simultaneously leveraging contextual readings to stimulate in-depth discussions on course-specific topics. Additionally, through collaboration with instructors, in several cases, JeepyTA’s prompts have been iteratively refined to better address specific learning objectives. Separate models have been employed to automate decisions on whether responses appear immediately or await instructor approval, providing finer control over JeepyTA’s engagement in forum discussions. Finally, JeepyTA’s behavior can be customised by category of tasks, giving instructors the flexibility to choose which topics or discussion forum categories it responds to and with what level of human supervision (human-in-the-loop).

Across courses, JeepyTA has been integrated into the open-source Flarum platform, appearing as a forum user distinctly marked as an AI Teaching Assistant. Through a Progressive Web App, the forum is accessible on mobile devices, allowing students and instructors to stay engaged on-the-go. In addition to traditional email notifications, users of a mobile app can receive push alerts – such as when JeepyTA responds or specifically mentions them – ensuring timely updates and facilitating faster interaction within the discussion forum.

Answer logistics questions

One of JeepyTA’s primary functions is answering logistics questions about the course. At the beginning of the semester, JeepyTA can handle enrollment-related inquiries, including prerequisites, add/drop deadlines, and options for changing course registration. When students need accommodations, JeepyTA directs them to official university guidelines and relevant support services. It also provides information on class schedules, classroom locations, and changes due to holidays or special events. When a course has multiple sections, JeepyTA helps students confirm where they need to attend.

To support coursework, JeepyTA clarifies submission guidelines for assignments, specifying required file formats, submission portals, and deadlines. JeepyTA also assists with technical aspects of online learning platforms when required by the course. It helps students log into external platforms used by instructors (for instance, for video discussions) and provides information like login codes, platform access links, and usage instructions. If students experience submission errors or other technical problems, JeepyTA offers guidance in many cases without needing to involve the instructor.

JeepyTA helps students understand grading policies by explaining how grades are calculated based on rubrics, weighted components, and participation requirements. It also assists in interpreting feedback from instructors and TAs and guiding students on resubmissions, appeals, or grade disputes (see Figure 9.1). When students need access to course materials, JeepyTA provides links to lecture slides, reading repositories, and virtual meeting links, ensuring they have the necessary resources.

With recent updates, JeepyTA can remember instructor responses and announcements on recurring topics. If students ask about schedule changes, assignment deadlines, or policy updates, JeepyTA provides the latest information. This reduces confusion and keeps students informed without requiring instructors to repeat themselves.

It is worth noting that JeepyTA’s ability to answer logistics questions depends on the information instructors choose to provide. It does not generate responses based on general knowledge but instead pulls from course-specific details that instructors input. If a detail was not provided, JeepyTA directs students to the human TA (if available for the course) and instructor or official course documents rather than guessing or giving incomplete information.

JeepyTA’s performance in answering logistics questions is not always perfect, as some student inquiries may go beyond what is covered in the course materials. Therefore, instructors can choose to edit JeepyTA’s responses at any time instead of only choosing between fully accepting or discarding them. This option is especially useful when combined with the feature that allows instructors to review JeepyTA’s response before it becomes visible to other students (as explained below). This flexibility allows instructors to keep useful parts, make quick edits, and provide students with accurate information while reducing effort.

Provide feedback to student essays

In several courses, JeepyTA provides feedback on student essays based on the grading rubric specified for the assignment. This consists of both higher-level conceptual elements and aspects of writing. In terms of higher-level conceptual elements, JeepyTA evaluates essays on the goals of the assignment, such as whether (for example) the student has appropriately discussed the needs of stakeholder groups, whether the student has made arguments in terms of theories discussed in class, or whether the limitations of a proposed solution have been concretely detailed (see Figure 9.2). When students submit drafts, JeepyTA acknowledges what they do well and highlights their strengths.

JeepyTA also offers feedback on more mechanical aspects of writing such as argument clarity, evidence use, structure, and writing quality (see Figure 9.2). In addition, JeepyTA comments on lower-level details such as language use, unclear phrasing, grammar mistakes, and wordiness. In these cases, it suggests revisions that can preserve the student’s original intent.

GPT models are trained to provide generic responses that apply across many scenarios, which can make their default feedback vague or overly general. To prevent this, JeepyTA was instructed to “provide actionable insights rather than shallow suggestions”. This small detail within prompt engineering makes a difference in helping students receive concrete guidance that improves their revisions.

If students need clarification, they can ask follow-up questions, and JeepyTA refines its guidance based on those questions. Instructors can also adjust JeepyTA’s feedback settings to focus on specific aspects of writing or emphasise areas where students generally struggle the most.

Before asking JeepyTA to provide feedback on essays, its responses are first tested on a set of sample essays and the output is reviewed with instructors. This step helps confirm that the feedback aligns with the pedagogical goals of the course. When necessary, the prompts are refined based on the instructor's suggestions in the review process. This process helps JeepyTA provide comments that are clear, relevant to the assignment, and focused on the aspects instructors consider most important. It also creates an opportunity to catch cases where the LLM’s default knowledge base produces inaccurate info, such as in cases where much of the content on the web reflects an incorrect understanding of a specific technical point.

In some cases, the prompt specifies a particular tone to shape the feedback style. For example, JeepyTA can be instructed to provide concise and direct feedback or take a more encouraging and supportive tone. This allows the feedback to align with the way instructors and TAs typically communicate with students about their writing. Additionally, past feedback from previous course offerings, along with de-identified student essays from matching pairs, are included in some cases as a reference for JeepyTA. JeepyTA does not use the content of past essays as a source for feedback but instead looks at these examples to follow the structure, level of detail, and key focus areas that instructors and TAs have emphasised. This helps make the feedback more useful to students by reflecting the expectations and priorities set in previous iterations of the course.