Automated Feedback to Peer Tutors Increases Their Talk Moves and Learner Reasoning but Not the Balance of Talk

Ranked by how close their title and abstract are to this paper’s. Select some to read them together.

Closest papers

1. Very close Same question Cited by this paper Not relevant

   Enhancing Tutoring Effectiveness Through Automated Feedback: Preliminary Findings from a Pilot Randomized Controlled Trial on SAT Tutoring

   Joy Yun, Yann Hicke, Mariah Olson et al. · 2024 · ACM Conference on Learning @ Scale · cited 7 times · Not summarized yet

   The pilot trial tests automated tutor feedback and student-facing feedback in SAT tutoring, the same intervention question examined here.

   Abstract

   To address educational inequities, high-quality SAT tutoring is crucial for students who need support. Many tutors, however, are novices and require coaching to be effective. To examine this issue, we conducted a pilot randomized controlled trial (RCT) to evaluate the effectiveness of personalized, automated feedback for novice tutors. This feedback aimed to enhance their tutoring skills and, consequently, improve student outcomes. In our RCT, we not only assessed the effectiveness of the feedback provided to novice tutors but also examined the impact of extending this feedback to both tutors and their students, compared to just the tutors alone. Furthermore, we explored how the use of socia…

2. Very close Another approach to the problem Cited by this paper Not relevant

   Can Automated Feedback Improve Teachers’ Uptake of Student Ideas? Evidence From a Randomized Controlled Trial in a Large-Scale Online Course

   Dorottya Demszky, Jing Liu, H. Hill et al. · 2023 · Educational Evaluation and Policy Analysis · cited 145 times · Not summarized yet

   M-Powering Teachers tests automated feedback for uptake of student contributions in an online computer science course rather than volunteer SAT tutoring.

   Abstract

   Providing consistent, individualized feedback to teachers is essential for improving instruction but can be prohibitively resource-intensive in most educational contexts. We develop M-Powering Teachers, an automated tool based on natural language processing to give teachers feedback on their uptake of student contributions, a high-leverage dialogic teaching practice that makes students feel heard. We conduct a randomized controlled trial in an online computer science course (N = 1,136 instructors), to evaluate the effectiveness of our tool. We find that M-Powering Teachers improves instructors’ uptake of student contributions by 13% and present suggestive evidence that it also improves stude…

3. Very close Another approach to the problem Cited by this paper Not relevant

   Does Feedback on Talk Time Increase Student Engagement? Evidence from a Randomized Controlled Trial on a Math Tutoring Platform

   Dorottya Demszky, Rose E. Wang, Sean Geraghty et al. · 2024 · International Conference on Learning Analytics and Knowledge · cited 9 times · Not summarized yet

   The Talk Meter tests whether automated talk-ratio feedback increases student talk in math tutoring, while this study targets tutor moves and learner reasoning.

   Abstract

   Providing ample opportunities for students to express their thinking is pivotal to their learning of mathematical concepts. We introduce the Talk Meter, which provides in-the-moment automated feedback on student-teacher talk ratios. We conduct a randomized controlled trial on a virtual math tutoring platform (n=742 tutors) to evaluate the effectiveness of the Talk Meter at increasing student talk. In one treatment arm, we show the Talk Meter only to the tutor, while in the other arm we show it to both the student and the tutor. We find that the Talk Meter increases student talk ratios in both treatment conditions by 13-14%; this trend is driven by the tutor talking less in the tutor-facing c…

4. Very close Broader context Not relevant

   Does Increased Choice over Learning Topic Improve the Effectiveness of Automated Feedback for Educators?

   Dorottya Demszky, Heather C. Hill, E. Taylor et al. · 2025 · Education sciences · Not summarized yet

   This trial tests whether giving volunteer instructors choice over automated feedback topics changes engagement, practice, or attendance.

   Abstract

   Educator agency in the form of choice over learning experiences is widely thought to enhance educator engagement and instructional improvement, yet causal evidence is scarce. We conducted a preregistered randomized controlled trial in an online computer science course with volunteer instructors who teach students worldwide. All instructors (N = 583) received automated feedback on their instruction, with half randomly assigned to have choice over the feedback topic. Choice alone did not increase feedback engagement or yield observable changes in practice, but it raised student attendance—an effect that was strongest for instructors who voluntarily engaged with additional training resources, i…

5. Close Same method, another problem Not relevant

   High School English Teachers Reflect on Their Talk: A Study of Response to Automated Feedback with the Teacher Talk Tool

   Sean Kelly, Gizem Guner, Nicholas C. Hunkins et al. · 2024 · International Journal of Artificial Intelligence in Education · cited 9 times · Not summarized yet

   The Teacher Talk Tool provides automated discourse feedback and studies teachers’ responses to it in English classrooms.

   Abstract

   We present the Teacher Talk Tool, which automatically analyzes classroom audio and provides formative feedback on key aspects of teachers’ classroom discourse (e.g., use of open-ended questions). The tool was designed to promote teacher learning by focusing attention and sense-making on their discourse. We conducted a feedback-response study where five English & Language Art teachers used the Teacher Talk Tool in eight classroom sessions. Teachers completed repeated-measure surveys and semi-structured interviews providing quantitative and qualitative evidence of feedback response. Results indicated that the majority of automated feedback was perceived to be accurate and prompted a high degre…

6. Close Broader context Not relevant

   Chat-Based Support Alone May Not Be Enough: Comparing Conversational and Embedded LLM Feedback for Mathematical Proof Learning

   Eason Chen, Sophia Judicke, Kayla Beigh et al. · 2026 · International Conference on Artificial Intelligence in Education · cited 6 times · Not summarized yet

   GPTutor compares embedded proof feedback with chatbot support for undergraduate mathematical proof learning, not feedback on tutor discourse.

   Abstract

   We evaluate GPTutor, an LLM-powered tutoring system for an undergraduate discrete mathematics course. It integrates two LLM-supported tools: a structured proof-review tool that provides embedded feedback on students'written proof attempts, and a chatbot for math questions. In a staggered-access study with 148 students, earlier access was associated with higher homework performance during the interval when only the experimental group could use the system, while we did not observe this performance increase transfer to exam scores. Usage logs show that students with lower self-efficacy and prior exam performance used both components more frequently. Session-level behavioral labels, produced by …

7. Close Same method, another problem Not relevant

   Scaling Teacher Professional Learning Through Automated Feedback: An Evaluation in K-12 Classrooms

   Jessica Vitale, Alyssa Van Camp, Sarah Miller et al. · 2026 · International Conference on Learning Analytics and Knowledge · cited 1 time · Not summarized yet

   This mixed-methods evaluation examines automated feedback on teaching practices and outcomes for K–12 teachers in authentic classrooms.

   Abstract

   Automated feedback provides a scalable approach to supporting teacher reflection and teaching practice. However, there is limited evidence on how these technologies impact multiple dimensions of teacher and student outcomes in authentic school settings. This mixed-methods study evaluated the implementation and short-term impacts of a 4–-5 week pilot providing K–-12 teachers with professional learning and automated feedback on teaching practices via the TeachFX application. We examined TeachFX’s impact on teacher well-being, teaching practice, classroom talk, and student engagement. Findings indicate that automated feedback is associated with reduced teacher burnout, improved self-efficacy, a…

8. Close Another approach to the problem Not relevant

   M-Powering Teachers: Natural Language Processing Powered Feedback Improves 1:1 Instruction and Student Outcomes

   Dorottya Demszky, Jing Liu · 2023 · ACM Conference on Learning @ Scale · cited 38 times · Not summarized yet

   M-Powering Teachers tests automated feedback on dialogic practices in one-to-one research mentorship rather than small-group tutoring.

   Abstract

   Although learners are being connected 1:1 with instructors at an increasing scale, most of these instructors do not receive effective, consistent feedback to help them improve. We deployed M-Powering Teachers, an automated tool based on natural language processing to give instructors feedback on dialogic instructional practices ---including their uptake of student contributions, talk time and questioning practices --- in a 1:1 online learning context. We conducted a randomized controlled trial on Polygence, a research mentorship platform for high schoolers (n=414 mentors) to evaluate the effectiveness of the feedback tool. We find that the intervention improved mentors' uptake of student con…

Prior work it builds on

1. Close* Same method, another problem Not relevant

   Automated feedback improves teachers' questioning quality in brick-and-mortar classrooms: Opportunities for further enhancement

   Dorottya Demszky, Jing Liu, Heather C. Hill et al. · 2024 · Comput. Educ. · cited 49 times · Not summarized yet

   This study examines automated feedback intended to improve teachers’ questioning in brick-and-mortar classrooms.

2. Close Broader context Not relevant

   From Tutor Moves to Tutoring States: Modeling the Timing and Sequencing of Pedagogical Strategies for Student Engagement

   Kirk P. Vanacore, Jinsook Lee, Bakhtawar Ahtisham et al. · 2026 · ACM Conference on Learning @ Scale · cited 1 time · Not summarized yet

   This study models the timing and sequencing of tutor moves in online math sessions rather than testing automated feedback.

   Abstract

   Understanding how tutoring unfolds requires capturing not only what tutors and students say, but also the multi-turn pedagogical strategies that structure their conversation. In this study, we analyze 77 online math tutoring sessions (about 40 hours of tutoring) using a combination of LLM-assisted annotation and statistical modeling. Building on utterance-level annotations with a taxonomy of tutor moves, we identify three recurrent latent states using a Hidden Markov Model: Inquiry Elicitation (dominated by prompting and probing student reasoning), Direct Instruction (centered on explanation and scaffolding), and Affective Support (characterized by praise and socioemotional support). Sequent…

3. Close Same question Not relevant

   Unpacking the Complexity: Why Current Feedback Systems Fail to Improve Learner Self-Regulation of Participation in Collaborative Activities

   Xavier Ochoa, Xiao-Meng Huang, Adam Charlton · 2024 · Journal of Learning Analytics · cited 4 times · Not summarized yet

   This mixed-methods study tests whether feedback changes learners’ participation behavior in collaborative activities.

   Abstract

   Even before the inception of the term learning analytics, researchers globally had been investigating the use of various feedback systems to support the self-regulation of participation and promote equitable contributions during collaborative learning activities. While some studies indicate positive effects for distinct subgroups of learners, a common finding is that the majority of learners do not modify their behaviour, even after repeated interventions. In this paper, we assessed one such system and, predictably, did not find measurable improvements in equitable participation. Informed by self-regulated learning theory, we conducted a mixed-methods study to explore the diverse paths that …

4. Close* Same method, another problem Not relevant

   Promoting rich discussions in mathematics classrooms: Using personalized, automated feedback to support reflection and instructional change

   Jennifer Jacobs, Karla Scornavacco, Charis Harty et al. · 2022 · Teaching and Teacher Education : An International Journal of Research and Studies · cited 88 times · Not summarized yet

   This work uses personalized automated feedback to prompt reflection and instructional change in mathematics classroom discussions.

5. Close Shared data, tool or method Not relevant

   A Deep Transfer Learning Approach to Modeling Teacher Discourse in the Classroom

   Emily Jensen, Samuel L. Pugh, Sidney K. D'Mello · 2021 · International Conference on Learning Analytics and Knowledge · cited 45 times · Not summarized yet

   This study compares machine-learning approaches, including BERT, for automatically modeling features of teacher discourse.

   Abstract

   Teachers, like everyone else, need objective reliable feedback in order to improve their effectiveness. However, developing a system for automated teacher feedback entails many decisions regarding data collection procedures, automated analysis, and presentation of feedback for reflection. We address the latter two questions by comparing two different machine learning approaches to automatically model seven features of teacher discourse (e.g., use of questions, elaborated evaluations). We compared a traditional open-vocabulary approach using n-grams and Random Forest classifiers with a state-of-the-art deep transfer learning approach for natural language processing (BERT). We found a tradeoff…