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

In a course on cultural foundations for teaching and learning offered in Fall 2024, JeepyTA interacted with students through personas, discussing scenarios and stories related to their cultural experiences with teaching and learning.

The persona prompts for JeepyTA consisted of three main components: persona description, situation, and instruction. The persona description section defines the persona’s name and role (e.g. Felipe, a teacher educator), the context in which the persona operates (e.g. teaching elementary education majors), and the personal and cultural background that reveals their funds of knowledge, such as home language, family activities, cultural rituals, and hobbies (Gonzalez, Moll and Amanti, 2006). The situation section contains the persona’s role in the interaction (e.g. providing feedback, answering questions, mentoring), the task or topic being discussed (e.g. reviewing drafts on a specific subject), and the participants in the conversation (e.g. the persona interacting with a preservice teacher). Finally, the instruction section provides information regarding the tone of voice (e.g. formal, informal, supportive, critical), the level of detail required in responses (e.g. detailed feedback with personal experiences), and specific elements to include, such as relevant examples.

For each persona defined on the forum, a separate sub-forum category was created using the persona's name. In these categories, JeepyTA responded while acting as the corresponding persona. Students were informed about the personas and instructed that JeepyTA would post there as the persona indicated by the category name.

Since the main goal of this course is to situate novice preservice teachers in culturally relevant and sustaining teaching pedagogies (Ladson-Billings, 1995; Paris, 2012), the persona descriptions intentionally emphasise the cultural and linguistic backgrounds to avoid generating general responses that do not centre specific groups of learners. Thus, the personas were developed based on the lived experiences of four individuals who were members of historically underrepresented groups whom the team interviewed (e.g. Mexican American, Hmong American). These individuals also reviewed the draft persona descriptions to ensure that their identities were accurately portrayed and to avoid racial essentialisation (Omi and Winant, 2018).

For instance, one persona, “Claire”, who identified as Hmong-American, shared personal stories related to food, spirituality, and family history, providing preservice teachers with concrete examples that fostered a deeper understanding of Hmong learners. Additionally, the personas guided preservice teachers in understanding concrete ways to incorporate funds of knowledge into classroom settings. For example, the persona “Felipe” suggested specific strategies for adapting class materials to align with Mexican-American families, such as incorporating family tree activities and introducing home craft projects (see Figure 9.7). These approaches offered practical and culturally responsive methods for connecting multicultural perspectives to classroom instruction.

Generate a discussion prompt to start the weekly discussion

One use case being piloted in Spring 2025 is the generation of discussion prompts to start weekly discussions based on the assigned readings. The goal is to provide a foundation for meaningful discussions while maintaining relevance to the course objectives.

When generating the discussion prompt, JeepyTA extracts core arguments, methodologies, and debates from the week’s readings. It identifies recurring themes, unresolved questions, or contrasting viewpoints and frames them in a way that encourages meaningful discussion. If students are expected to relate the reading material to their own research, JeepyTA includes questions that prompt reflection on personal experiences or future applications. If the goal is to explore methodological issues, it focuses on the strengths, limitations, and assumptions underlying the methods presented in the readings (see Figure 9.8).

Some prompts JeepyTA suggests may not be immediately useful. For example, it has generated questions that are overly broad and more at the level of the entire course than a specific week’s content. Other discussion questions generated may be too complex and require extensive background knowledge or additional explanation before students can engage with them. A highly detailed methodological critique, for example, may be difficult to address within the scope of a discussion forum. Therefore, instructors and TAs have reviewed all of the discussion prompts before making them visible to students. JeepyTA’s suggestions have provided a starting point that allows instructors and TAs to refine the wording, adjust the focus, or simplify overly technical questions to improve understandability. As such, JeepyTA does not replace instructor or TA expertise in orchestrating discussions, but rather, it streamlines the process by offering an initial draft that helps structure each week’s forum.

Evaluation of JeepyTA

JeepyTA was first implemented in Fall 2023 and covered use cases described above. To understand how students viewed the virtual teaching assistant, we distributed a voluntary end-of-semester survey, approved as exempt by the university’s Institutional Review Board, and clearly stated that participation was optional and would not affect grades. Students provided informed consent before answering 13 multiple-choice questions. The survey asked about specific aspects of JeepyTA’s performance, including how quickly and accurately it responded to questions, how clearly and professionally it communicated, and how well it supported student learning, development, and motivation, compared to a human TA. Response options ranged from 1 ("Human TA is significantly better") to 5 ("AI TA is significantly better"). We used two-sample t-tests to check whether the average scores for each question differed from the neutral midpoint of 3. This allowed us to measure whether students viewed JeepyTA as better or worse than a human TA. Results showed that students rated JeepyTA as comparable to a human TA in several areas, including the speed and clarity of its replies, the accuracy and professionalism of its responses, its ability to support learning without giving away answers, and the overall usefulness and quality of its feedback. However, students rated it lower in three areas: offering useful ideas, supporting student development, and motivating students.

We also evaluated JeepyTA’s impact on when responses were available to students. In two identical courses offered one year apart, taught by the same professor at the same institution and involving comparable student groups, a difference emerged in response times to students’ queries. During the earlier term, when JeepyTA was not in use, the instructor and the TAs posted 153 responses, with a median response time of 7.09 hours. In the following term of Fall 2023, after JeepyTA was introduced, course staffposted 136 responses, and the median response time dropped to 2.23 hours, a statistically significantly lower time.

Among 89 posts where JeepyTA attempted to answer student inquiries, 22 responses received approval. The AI generated replies in approximately 40 seconds, and course staffapproved them within an average of 38 minutes. Because JeepyTA was able to handle these queries, even manually written responses were faster. In the JeepyTA semester, the median human response time was 4.14 hours, statistically significantly faster than the 7.09-hour median time observed in the prior semester. This suggests that JeepyTA improved the efficiency of even fully-human responses, likely because the instructor and TAs had more time to address tasks that deserved more of their attention.

Another of JeepyTA’s intended purpose was to support instructors and TAs outside standard working hours. In the term prior to its introduction, course staffposted 62% of replies outside regular U.S. business hours (after 5 p.m. and before 9 a.m.). In the term when JeepyTA was available, this proportion was 60%, not statistically significantly different. However, there appeared to be a difference in the proportion of responses posted during weekends. In the prior semester, instructors posted 10% of all replies on weekends. After JeepyTA was introduced, this number increased to 29% of the total number of replies (including JeepyTA posts approved or edited by the instructors). This increase was statistically significant, suggesting that course staffwere better able to focus their work time even on weekends, following the introduction of JeepyTA.

Work is currently ongoing to study the impact of JeepyTA’s feedback on student essays (3b). In our initial work, we have found that semester-on-semester, students receive statistically significantly higher assignment grades (according to an independent grader) after receiving JeepyTA’s feedback – going from an average of 64% of students receiving an A or an A+ on their final submission to 95% of students receiving an A or A+. In follow-up work, we are investigating whether students specifically fix the issues identified by JeepyTA in that same essay, and whether they make the same mistakes in subsequent essays (including in a different class where JeepyTA is also offered).

A study on JeepyTA’s brainstorming support functionality (3f) (Shah et al., 2024) revealed that JeepyTA helped students generate a higher volume of ideas (averaging 2.78 per student compared to 1.7 for student-led ideation) and increased the production of fully formed, detailed concepts. However, this came with important trade-offs: JeepyTA-driven ideas often showed thematic overlap, with common suggestions like "teamwork and collaboration" appearing across multiple students' work, while student-driven ideas exhibited greater diversity (cf. Doshi and Hauser, 2024). In analysing students’ descriptions of their process of using JeepyTA, the researchers identified five distinct patterns: 36% of students found the suggestions insightful and aligned with their own ideas, 18% acknowledged JeepyTA's role in idea generation but did not say if it was actually helpful in doing so, 18% referenced using JeepyTA but did not say how/if its use was connected to their proposed ideas, 18% made no reference to JeepyTA, and 13% critically evaluated and built upon JeepyTA’s recommendations. Notably, 68% of students proposed multiple educational applications spanning knowledge types essential for 21st-century learning (Kereluik et al., 2013): meta- knowledge (collaboration, problem-solving), foundational knowledge (mathematics, history, computer science), and humanistic knowledge (digital citizenship, ethical awareness). Especially novel recommendations emerged when students integrated insights from readings, game experiences, and their domain expertise, suggesting that JeepyTA works best as a brainstorming tool when students already possess foundational knowledge they can build upon (Nasiar, forthcoming).