Is ChatGPT a Good Teacher Coach? Measuring Zero-Shot Performance For Scoring and Providing Actionable Insights on Classroom Instruction
Year: 2023

Abstract:
Coaching, which involves classroom observation and expert feedback, is a widespread and fundamental part of teacher training. However, the majority of teachers do not have access to consistent, high quality coaching due to limited resources and access to expertise. We explore whether generative AI could become a costeffective complement to expert feedback by serving as an automated teacher coach. In doing so, we propose three teacher coaching tasks for generative AI: (A) scoring transcript segments based on classroom observation instruments, (B) identifying highlights and missed opportunities for good instructional strategies, and (C) providing actionable suggestions for eliciting more student reasoning. We recruit expert math teachers to evaluate the zero-shot performance of ChatGPT on each of these tasks for elementary math classroom transcripts. Our results reveal that ChatGPT generates responses that are relevant to improving instruction, but they are often not novel or insightful. For example, 82% of the model’s suggestions point to places in the transcript where the teacher is already implementing that suggestion. Our work highlights the challenges of producing insightful, novel and truthful feedback for teachers while paving the way for future research to address these obstacles and improve the capacity of generative AI to coach teachers.1

heading: 1 Introduction
text: Classroom observation, coupled with coaching, is the cornerstone of teacher education and professional development internationally (Adelman and Walker, 2003; Wragg, 2011; Martinez et al., 2016; Desimone and Pak, 2017). In the United States, teachers typically receive feedback from school administrators or instructional coaches, who assess teachers based on predetermined criteria and
1The code and model outputs are open-sourced here: https://github.com/rosewang2008/ zero-shot-teacher-feedback.
rubrics. These structured evaluations often involve pre- and post-observation conferences, where the observer and teacher discuss teaching strategies and reflect on the observed instruction.
Despite its widespread adoption, classroom observation lacks consistency across schools and different learning contexts due to time and resource constraints, human subjectivity, and varying levels of expertise among observers (Kraft et al., 2018; Kelly et al., 2020). Frequency and quality of feedback can vary significantly from one school or learning context to another, resulting in disparities in teacher development opportunities and, consequently, student outcomes.
Prior work has sought to complement the limitations of manual classroom observation by leveraging natural language processing (NLP) to provide teachers with scalable, automated feedback on instructional practice (Demszky et al., 2023a; Suresh et al., 2021). These approaches offer low-level statistics of instruction, such as the frequency of teaching strategies employed in the classroom— different from the high-level, actionable feedback provided during coaching practice. Receiving highlevel, actionable feedback automatically could be easier for teachers to interpret than low level statistics, and such feedback also aligns more closely with existing forms of coaching.
Recent advances in NLP have resulted in models like ChatGPT that have remarkable few-shot and zero-shot abilities. ChatGPT has been applied to various NLP tasks relevant to education, such as essay writing (Basic et al., 2023) or assisting on mathematics problems (Pardos and Bhandari, 2023), and providing essay feedback to students (Dai et al., 2023). A survey conducted by the Walton Family Foundation shows that 40% of teachers use ChatGPT on a weekly basis for tasks such as lesson planning and building background knowledge for lessons (Walton Family Foundation, 2023). Given ChatGPT’s potential and teachers’ growing
626
CLASS 15-minute transcript segment Data
Observation item
Evaluation
Score prediction
CLPC CLBM CLINSTD
Highlights
Missed opportunities
Prompting Task
MQI 7.5-minute transcript segment
EXPL REMED LANGIMP SMQR
SuggestionsScore prediction Highlights
Missed opportunities
Experienced human teachers Experienced human teachersHuman ratingsHuman ratings
C
B
AA
B
"# $
QuantitativeQualitativeQuantitative Qualitative
"# $
Figure 1: Setup for the automated feedback task. Our work proposes three teacher coaching tasks. Task A is to score a transcript segment for items derived from classroom observation instruments; for instance, CLPC, CLBM, and CLINSTD are CLASS observation items, and EXPL, REMED, LANGIMP, SMQR are MQI observation items. Task B is to identify highlights and missed opportunities for good instructional strategies. Task C is to provide actionable suggestions for eliciting more student reasoning.
familiarity with it, we are interested in the following research question: Can ChatGPT help instructional coaches and teachers by providing effective feedback, like generating classroom observation rubric scores and helpful pedagogical suggestions?
To answer this question, we propose the following teacher coaching tasks for generative AI.
Task A. Score a transcript segment for items derived from classroom observation instruments
Task B. Identify highlights and missed opportunities for good instructional strategies
Task C. Provide actionable suggestions for eliciting more student reasoning
We evaluate the performance of ChatGPT with zero-shot prompting on each of these tasks via the process in Figure 1. We use the NCTE dataset (Demszky and Hill, 2022), a large dataset of elementary math classroom transcripts. The data is annotated by experts with two observation protocols: the Classroom Assessment Scoring System (CLASS) (Pianta et al., 2008) and Mathematical Quality Instruction (MQI) (Hill et al., 2008) instruments. We prompt ChatGPT to score segments from these transcripts (Task A) and to identify highlights and missed opportunities (Task B) with respect to items derived from CLASS and MQI. Finally, we prompt the model to generate suggestions
to the teacher for eliciting more student mathematical reasoning in the classroom (Task C). We evaluate ChatGPT by comparing the model’s numerical predictions to raters’ scores in the NCTE data (Task A). We also recruit math teachers to rate the ChatGPT’s responses along multiple helpfulness criteria (Tasks B & C).
We find that ChatGPT has significant room for improvement in all three tasks, but still holds promise for providing scalable high-quality feedback. On predicting scores, ChatGPT has low correlation with human ratings across all observation items even with added rubric information and reasoning. On identifying highlights and missed opportunities, ChatGPT generates responses that are often not insightful (50-70%) or relevant (35-50%) to what is being asked for by both instruments. Finally, the majority of suggestions generated by ChatGPT (82%) describe what the teacher already does in the transcript. Nonetheless, the model does generate a majority of suggestions that are actionable and faithfully interpret the teaching context. We believe that with further development, ChatGPT can become a valuable tool for instructional coaches and teachers. Our work highlights an exciting area for future research to improve on the current limitations of automated feedback systems.
In sum, we make the following contributions: we (1) propose three teacher coaching tasks for
generative AI, (2) recruit expert teachers to evaluate ChatGPT’s zero-shot performance on these tasks given elementary math classroom transcripts, (3) demonstrate that ChatGPT is useful in some aspects but still has a lot of room for improvement, and finally (4) highlight directions for future directions towards providing useful feedback to teachers.

heading: 2 Related Work
text: Automated feedback to educators. Prior works on automated feedback tools provide analytics on student engagement and progress (Su et al., 2014; Schwarz et al., 2018; Aslan et al., 2019; BonnetonBotté et al., 2020; Alrajhi et al., 2021, among others). These tools enable teachers to monitor student learning and intervene as needed. Recent NLP advances are able to provide teachers feedback on their classroom discourse, promoting selfreflection and instructional development (Samei et al., 2014; Donnelly et al., 2017; Kelly et al., 2018; Jensen et al., 2020). For example, Suresh et al. (2021) provides feedback to teachers on their teaching moves, such as how frequently the teacher revoices a student’s idea or how frequently the teacher asks students to reason aloud. Jacobs et al. (2022) provides evidence that K-12 math teachers receive this kind of feedback positively. A similar tool, M-Powering Teachers, provides feedback to teachers on their uptake of student ideas and demonstrates effectiveness in the 1-on-1 learning setting (Demszky and Liu, 2023). and online group instruction Demszky et al. (2023b). Altogether, these findings show a positive impact of cost-effective automated tools. They prompt further investigations into what other types of automated feedback are effective. Our work constitutes one exploration in this area.
Testing zero-shot capabilities of ChatGPT. Recent works have measured the capabilities of ChatGPT for annotation on established datasets and benchmarks (Kuzman et al., 2023; He et al., 2023; Gilardi et al., 2023; Dai et al., 2023). For example, in a non-education setting, Gilardi et al. (2023) evaluates the zero-shot ability of ChatGPT to classify tweets. Dai et al. (2023) is a recent education work that investigates ChatGPT’s zero-shot ability to provide feedback to students on business project proposals. However, their study only utilizes a single broad prompt to solicit feedback and they do not evaluate for common model issues like hallucination (Ji et al., 2023). Our work proposes
three concrete tasks to generate different forms of feedback for teachers, and our evaluation targets common qualitative issues in model generations. For other recent applications of ChatGPT, we refer the reader to Liu et al. (2023).

heading: 3 Data
text: We use the National Center for Teacher Effectiveness (NCTE) Transcript dataset (Demszky and Hill, 2022) in this work, which is the largest publicly available dataset of U.S. classroom transcripts linked with classroom observation scores. The dataset consists of 1,660 45-60 minute long 4th and 5th grade elementary mathematics observations collected by the NCTE between 2010-2013. The transcripts are anonymized and represent data from 317 teachers across 4 school districts that serve largely historically marginalized students.
Transcripts are derived from video recordings, which were scored by expert raters using two instruments at the time of the NCTE data collection: the Classroom Assessment Scoring System (CLASS) (Pianta et al., 2008) and Mathematical Quality Instruction (MQI) (Hill et al., 2008) instruments. We evaluate ChatGPT’s ability to predict scores for both instruments, as described below.
The CLASS instrument. CLASS is an observational instrument that assesses classroom quality in PK-12 classrooms along three main dimensions: Emotional Support, Classroom Organization and Instructional Support. Each of these dimensions is measured by multiple observation items; we choose one item from each dimension to provide a proofof-concept. For Emotional Support, we focus on the POSITIVE CLIMATE (CLPC) item, which measures the enjoyment and emotional connection that teachers have with students and that students have with their peers. For Classroom Organization, we focus on the BEHAVIOR MANAGEMENT (CLBM) item which measures how well the teachers encourage positive behaviors and monitor, prevent and redirect misbehavior. Finally, for Instructional Support, we focus on the INSTRUCTIONAL DIALOGUE (CLINSTD) dimension which measures how the teacher uses structured, cumulative questioning and discussion to guide and prompt students’ understanding of content. Each item is scored on a scale of 1-7 where 1 is low and 7 is high. All items are scored on a 15-minute transcript segment, which is typically about a third or fourth of the full classroom duration.
The MQI instrument. The MQI observation instrument assesses the mathematical quality of instruction, characterizing the rigor and richness of the mathematics in the lesson, along four dimensions: Richness of the Mathematics, Working with Students and Mathematics, Errors and Imprecision, and Student Participation in Meaning-Making and Reasoning. Similar to CLASS, each of these dimensions is measured by several observation items and we select one from each. For Richness of the Mathematics, we focus on the EXPLANATIONS (EXPL) dimension which evaluates the quality of the teacher’s mathematical explanations. For Working with Students and Mathematics, we focus on the REMEDIATION OF STUDENT ERRORS AND DIFFICULTIES (REMED) which measures how well the teacher remediates student errors and difficulties. For Errors and Imprecision, we focus on the IMPRECISION IN LANGUAGE OR NOTATION (LANGIMP) dimension which measures the teacher’s lack of precision in mathematical language or notation. Finally, for Student Participation in Meaning-Making and Reasoning, we focus on the STUDENT MATHEMATICAL QUESTIONING AND REASONING (SMQR) dimension which measures how well students engage in mathematical thinking. These items are scored on scale of 1-3 where 1 is low and 3 is high. They are scored on a 7.5 minute transcript segment, which is typically a seventh or eighth of the full classroom duration.

heading: 3.1 Pre-processing
text: Transcript selection. Due to classroom noise and far-field audio, student talk often contains inaudible talk marked as “[inaudible]”. In preliminary experiments, we notice that ChatGPT often overinterprets classroom events when “[inaudible]” is present in the student’s transcription. For example, the model misinterprets the transcription line “student: [inaudible]” as “ A student’s response is inaudible, which may make them feel ignored or unimportant.” or the line “Fudge, banana, vanilla, strawberry, banana, vanilla, banana, [inaudible]. [...]” as the teacher allowing students to talk over each other and interrupt the lesson. To reduce the occurrences of the model overinterpreting the classroom events and best evaluate the model’s ability to provide feedback, we only consider transcripts where less than 10% of the student contributions includes an “[inaudible]” marker. Because these transcripts are very long and it would be costly to
evaluate ChatGPT on all of the transcripts, we randomly pick 10 for the CLASS instrument and 10 for the MQI instrument to use.
Transcript segmentation. The CLASS observation instrument applies to 15-minute segments and MQI to 7.5-minute segments. Each transcript has an annotation of the total number of CLASS segments and MQI segments. We split each transcript into segments by grouping utterances into equal-sized bins. For example, if a transcript has 3 CLASS segments and 300 utterances, we each segment will have 100 utterances each.
Segment formatting. In the quantitative Task A experiments, every utterance in the transcript segment is formatted as: “<speaker>: <utterance>”. <speaker> is either the teacher or a student and <utterance> is the speaker’s utterance. In our qualitative Task B and C experiments, we mark every utterance with a number. The utterance is formatted as: “<utterance number>. <speaker>: <utterance>”. We use utterance numbers in the qualitative experiments because our prompts ask the model to identify utterances when providing specific feedback. In contrast, the quantitative experiments evaluate the entire transcript segment holistically.

heading: 4 Methods
text: We use the gpt-3.5-turbo model through the OpenAI API, the model that powers ChatGPT. We decode with temperature 0. We employ zero-shot prompting in our study for three reasons. First, transcript segments are long, and the length of annotated example segments would exceed the maximum input size. Second, zero-shot prompting mimics most closely the current ways in which teachers interact with ChatGPT. Third, we are interested in evaluating ChatGPT’s capabilities off-the-shelf, without additional tuning.

heading: 4.1 Prompting
text: We provide an overview of prompting methods. Appendix A contains all the prompts used in this work and information about how they are sourced.
Task A: Scoring transcripts. We zero-shot prompt ChatGPT to predict observation scores according to the CLASS and MQI rubrics. We employ three prompting techniques: (1) prompting to directly predict a score with 1-2 sentence summary of the item (direct answer, DA) – see example for
CLBM in Figure 6, (2) same as DA but with additional one-sentence descriptions for low/mid/high ratings (direct answer with description, DA+) and (3) same as DA, with asking the model to provide reasoning before predicting a score (reasoning then answer, RA). RA follows recent literature on LLM prompting with reasoning where models benefit from added reasoning on mathematical domains (Wei et al., 2022, inter alia). The item descriptions all derived from the original observation manuals, condensed to fit the context window of the model while accounting for space taken up by the transcript segment. For all the prompts, the model correctly outputs integer values within each observation instrument’s score range.
Task B: Identify highlights and missed opportunities. We zero-shot prompt ChatGPT to identify and elaborate on highlights and missed opportunities for CLASS and MQI items. Specifically, we prompt ChatGPT to identify 5 good and bad examples (i.e. missed opportunities or poor execution) of each dimension. The prompt includes numbered transcript sentences and asks the model to indicate the line number, before explaining the example. See Figure 2 for an example of the prompt and model outputs.
Task C: Provide actionable suggestions for eliciting student reasoning. We zero-shot prompt ChatGPT to provide actionable suggestions to the teacher for eliciting more student mathematical reasoning in the classroom. This task is open-ended: We simply ask the model to provide 5 suggestions for the teacher for eliciting more student reasoning in the classroom. We provide the model with a definition of student reasoning. The prompt includes numbered transcript sentences and asks the model to indicate the line number, before explaining the example. We use the MQI transcript segments because they are shorter and more likely to fit within the model’s context window. See Figure 3 for an example of the prompt and model outputs.

heading: 4.2 Validation
text: We describe the analytical methods we use to answer each of the research questions.
Task A. The NCTE transcript dataset contains CLASS and MQI scores from human annotators. We compare ChatGPT’s predictions against the human annotator scores. We randomly pick 100 transcript segments from the processed dataset (rf.
Section 3.1) for evaluation. We compute Spearman correlation to measure how well the model’s predictions correspond to the human ratings. We also compare the distribution of human ratings vs model ratings via a histogram, to understand how
well ChatGPT is calibrated for this task.
Task B. We randomly pick 10 transcript segments and prompt the model to identify highlights and missed opportunities per observation item in CLASS and MQI. We randomly select two high-
lights and two missed opportunities to be evaluated. This results in 216 CLASS examples (= 18 segments ×3 CLASS codes × (2 highlights +2 missed opportunities)) and 288 MQI examples (= 18 segments ×4 MQI codes × (2 highlights +2 missed opportunities)). We recruit two math teachers to evaluate the model’s outputs: one of the teachers has decades of experience as an instructional coach, and the other has 6 years of math teaching experience in title 1 public schools. Examples were split evenly between the teachers.
Teacher are asked to rate each example along three criteria, which we identify based on preliminary experiments (e.g. observed hallucination) and by consulting the teachers.
1. Relevance: Is the model’s response relevant to the CLASS or MQI item of interest?
2. Faithfulness: Does the model’s response have an accurate interpretation of the events that occur in the classroom transcript?
3. Insightfulness: Does the model’s response reveal insights beyond a literal restatement of what happens in the transcript?
Each criteria is evaluated on a 3-point scale (yes, somewhat, no) with optional comments. For more details on the experimental setup and interrater comparison, please refer to Appendix B.
Task C. We evaluate this task similarly to Task B, except for slight changes in the criteria. We prompt the model using the 18 transcript segments from Task B to generate suggestions for eliciting more student reasoning. We randomly sample 2 suggestions per segment, resulting in 36 examples. Examples were split evenly between annotators. We use the following evaluation criteria:
1. Relevance: Is the model’s response relevant to eliciting more student reasoning?
2. Faithfulness: Does the model’s response have the right interpretation of the events that occur in the classroom transcript?
3. Actionability: Is the model’s suggestion something that the teacher can easily translate into practice for improving their teaching or encouraging student mathematical reasoning?
4. Novelty: Is the model suggesting something that the teacher already does or is it a novel suggestion? Note that the experimental interface asks
about “redundancy”; we reverse the rating here for consistency across criteria (higher= better).
Similar to the previous section, we ask the teachers to evaluate on a 3-point scale (yes, somewhat, no) with optional comments.

heading: 5 Results & Discussion
text: Task A: Scoring transcripts. ChatGPT performs poorly at scoring transcripts both for MQI and CLASS items. Table 1 reports the Spearman correlation values, and Figure 4 reports the score distributions. Appendix C contains additional plots, including a comparison of the human vs. model score distributions.
As for CLASS, two findings are consistent across our prompting methods. First, the the model tends to predict higher values on all CLASS dimensions than human ratings and it performs best on CLBM. We hypothesize that CLBM may be easier to predict because (i) it is the only item whose distribution is skewed towards higher values and (ii) because scoring behavior management requires the least pedagogical expertise. Interestingly, adding more information to the prompt like per-score descriptions (DA+) or allowing for reasoning (RA) did not improve the correlation score—in some cases making the score worse, such as for CLBM.
As for MQI, for all dimensions but REMED the model tends to predict the middle score (2 out of 3); this observation is consistent across all prompting methods. Another interpretation of this finding, consistent with the CLASS results (which is on a 7 point scale), is that the model tends to predict the
second to highest rating. We do not have sufficient data to disentangle these two interpretations.
For REMED, the model generally predicts the highest rating (Figure 4). Similar to the observations made in CLASS, adding more information or reasoning does not help the model. The model seems to pick up on SMQR better than the other items, but its correlation decreases with both added information and reasoning.
Altogether, the models’ tendency to predict the same scores for the same MQI or CLASS item suggest that the predicted scores are a function of the dimension description and not of the transcript evidence or the prompting methodology.
Task B: Identify highlights and missed opportunities. Figure 5a summarizes the ratings on model responses for the CLASS instrument, and Figure 5b for the MQI instrument. Teachers generally did not find the model responses insightful or relevant to what was being asked for both instruments. Hallucination, as rated by faithfulness, is not the most problematic dimension out of the three. Nonetheless, it appears in a nontrivial amount of the model responses—around 20-30% of the model responses are marked with being unfaithful in interpreting the classroom transcript.
Interestingly, the MQI results are worse than the CLASS results across all evaluation dimensions. Concretely, the “No” proportions increase on every dimension from CLASS→MQI: Low scores on faithful increase 22 → 29% (+7), relevant 35 → 55% (+20), and insightful 51 → 71% (+20). This suggests that the model performs relatively worse on interpreting and evaluating technical aspects of math instruction quality. Appendix C contains additional plots, including the Cohen’s kappa between raters.
Task C: Provide actionable suggestions for eliciting student reasoning. Figure 5c summarizes the ratings on the model suggestions. The most noticeable observation is that the model tends to produce redundant suggestions (opposite of novelty), repeating what the teacher already does in the transcript 82% of the time. Nonetheless, most model responses were rated to be faithful to the transcript context, relevant to eliciting more student reasoning, and actionable for the teacher to implement.
The results for Task B and C may be explained by the fact that ChatGPT was unlikely to see exam-
ples of instructional feedback, let alone examples of teacher coaching during its training, given the scarcity of publicly available data in this area. Thus, it has only learned to reproduce patterns already observed in the text, and not to produce out-of-thebox expert suggestions.

heading: 6 Limitations
text: This section discusses the limitations related to the evaluation process and potential ethical considerations associated with the use of ChatGPT or similar language models in educational settings.
Human evaluation Our evaluation is conducted with a limited sample size of two teachers. Future work should aim to include a larger and diverse sample of teachers to capture a wider range of perspectives. This would help tease apart the potential teacher biases from generalizable claims about the feedback quality.
Ethical considerations The use of language models like ChatGPT in educational contexts war-
rants careful examination. For example, because the model relies on transcribed speech and is trained on primarily English, it might misinterpret the transcriptions of teachers or students who do not speak English fluently. Additionally, deploying language models in education settings raises concerns regarding privacy and data security. For example, the raw classroom transcripts should not be directly fed into the model to provide feedback as it may contain personally identifiable information about students. Guardrails should be set to prevent classroom data from being sent directly to external companies.

heading: 7 Avenues for Future Work
text: As evidenced from our work, generating good feedback for teaching is challenging and ChatGPT has significant room for improvement in this area. This section discusses potential future directions to overcome these obstacles.
Reducing hallucination. Our results show that ChatGPT does generate a non-trivial amount of misleading responses as measured by our faithfulness dimension (15-30% of the time). This observation is documented in the LLM literature as model hallucination (Ji et al., 2023). In domains that leverage references or citations such as in factchecking, remedies include retrieving sources and checking the claims made by the model (Nakano et al., 2022; Menick et al., 2022, inter alia). In the domain of teacher feedback, however, it is not obvious what the “true” interpretation is, as even human observers may disagree slightly with respect to the teachers’ intentions or actions. Future work could decrease hallucination in these higher inference domains, e.g. by forcing the model to be conservative with respect to making inferences.
Involving coaches and educators in model tuning. Our results show that ChatGPT struggles to generate insightful and novel feedback for teachers; understandably, since such feedback is not present in its training data. Involving coaches and educators in the reinforcement learning stage of model fine-tuning (Christiano et al., 2017) could be an effective way to improve the models’ performance for teacher coaching. One less costly alternative is to engineer the model’s prompt collaboratively with teachers and coaches. However, we are sceptical about the effectiveness of prompt engineering for teacher feedback, as it does not address model’s lack of exposure to teacher coaching examples during training.
Tailoring feedback to a teacher’s needs and expanding to other subjects. What counts as helpful feedback may be different for each teacher, and look different in other subjects, eg. History and English. Even for the same teacher, what they self-report to be helpful may be different from what what has a positive impact on their practice. An effective coach takes this into account, and is able to dynamically adapt the feedback based on the teacher’s needs and based on what they observe to be effective for that teacher (Thomas et al., 2015; Kraft and Blazar, 2018). Improving ChatGPT’s ability to differentiate feedback based on the teacher’s needs, and update the feedback strategy based on teacher’s subsequently observed practice would be a valuable direction for future work.
To adapt our approach beyond mathematics, such as in subjects like History or English, re-
searchers and instructors should collaborate and account for the subject’s instructional practices and learning objectives. This would help identify the relevant dimensions of effective teaching and inform the design of feedback prompts. For example, they can build on the subject-specific observation instruments as done in our work.
Integrating automated feedback into human coaching practice. We envision automated coaching to complement, rather than replace coaching by experts for three reasons. First, as this paper shows, the capabilities of current technology is very far from that of an expert instructional coach. Second, even with improved technology, having an expert in the loop mitigates the risks of misleading or biased model outputs. Finally, even though automated feedback offers several benefits, including flexibility, scalability, privacy, lack of judgment, human interaction is still an important component of coaching and is perceived by teachers as such (Hunt et al., 2021). Automated coaching could complement human coaching in a teacher-facing way, e.g. by directly providing the teacher with feedback on-demand. Such an automated tool can also be coach-facing, e.g. by generating diverse range of suggestions that the coach can then choose from based on what they think is most helpful for the teacher they are supporting.

heading: 8 Conclusion
text: Our work presents a step towards leveraging generative AI to complement the limitations of manual classroom observation and provide scalable, automated feedback on instructional practice. While our results reveal that ChatGPT has room for improvement in generating insightful and novel feedback for teaching, our proposed tasks and evaluation process provide a foundation for future research to address the challenges of teacher coaching using NLP. Our work underscores the challenge and importance of generating helpful feedback for teacher coaching. Moving forward, we propose several directions for further research, such as improved prompting methods and reinforcement learning with feedback from coaches. Ultimately, we envision a future where generative AI can play a crucial role in supporting effective teacher education and professional development, leading to improved outcomes for students.

heading: Acknowledgements
text: REW is supported by the National Science Foundation Graduate Research Fellowship. We thank Jiang Wu and Christine Kuzdzal for their helpful feedback.

heading: A Prompts and decoding parameters
text: This section provides all the prompts we used in our work and decoding parameters with using ChatGPT/gpt-3.5-turbo. We used the OpenAI API to send queries to ChatGPT. We sampled from the model with temperature 0.
The subsections include the prompts for (a) scoring the teacher according to the CLASS and MQI rubric, (b) identifying highlights and missed opportunities and (c) providing actionable insights for teachers.
A.1 Observation scores We prompt ChatGPT to provide scores according to the CLASS and MQI rubrics.
Prompts for directly predicting the scores are shown in:
• Figure 8 for CLPC.
• Figure 9 for CLBM
• Figure 10 for CLINSTD
• Figure 11 for EXPL
• Figure 12 for REMED
• Figure 13 for LANGIMP
• Figure 14 for SMQR
Prompts for directly predicting the scores with additional rubric descriptions are shown in:
• Figure 15 for CLPC.
• Figure 16 for CLBM
• Figure 17 for CLINSTD
• Figure 18 for EXPL
• Figure 19 for REMED
• Figure 20 for LANGIMP
• Figure 21 for SMQR
Prompts for reasoning then predicting the scores are shown in:
• Figure 22 for CLPC.
• Figure 23 for CLBM
• Figure 24 for CLINSTD
• Figure 25 for EXPL
• Figure 26 for REMED
• Figure 27 for LANGIMP
• Figure 28 for SMQR
A.2 Highlights and missed opportunities We prompt ChatGPT to identify highlights and missed opportunities according to the CLASS and MQI dimensions. The prompts for each dimension are shown in:
• Figure 29 for CLPC
• Figure 30 for CLBM
• Figure 31 for CLINSTD
• Figure 32 for EXPL
• Figure 33 for REMED
• Figure 34 for LANGIMP
• Figure 35 for SMQR
A.3 Actionable suggestions We prompt ChatGPT to make actionable suggestions to the teacher for eliciting more student mathematical reasoning in the classroom. The prompt used for this task is shown in Figure 36.

heading: B Human experiments
text: We recruited 2 experienced human teachers to evaluate the generated model responses. As illustrated in our main figure (Figure 1), there are three main responses that are being evaluated by the human teachers: the highlights, missed opportunities and suggestions. Every observation code has their own generated highlights and missed opportunities.
B.1 Collecting model responses to evaluate Highlights and missed opportunities From the transcripts which have less than 10% student contributions including “[inaudible]” markers, we sample 18 random 15-minutes transcript segments for the CLASS codes, and 18 random 7.5 minutes tran-
script segments for the MQI codes. Every code has 2 model-generated highlights and missed opportunities. In total, we have 216 CLASS-annotated items. The calculation is: 18 segments ×3 CLASS codes × (2 highlights +2 missed opportunities) = 216 items. In total, we have 288 MQI-annotated items. The calculation is: 18 segments ×4 MQI codes × (2 highlights +2 missed opportunities) = 288 items.
Suggestions We use the same 18 random MQI 7.5-minutes transcript segments for prompting the model for suggestions. In total, we have 36 item suggestions. The calculation is 18 segments ×2 suggestions = 36 items.
B.2 Evaluation axes and human interface This section details what we ask the teachers to evaluate qualitatively. Some of the details are repeated from Section 4.2 for completeness. We additionally include screenshots of the human experiment interface.
Highlights and missed opportunities The teachers evaluate the model examples along three axes. One is relevance: Is the model’s response relevant to the CLASS or MQI dimension of interest? Two is faithfulness: Does the model’s response have the right interpretation of the events that occur in the classroom transcript? We evaluate along this dimension because the model sometimes can hallucinate or misinterpret the events in the transcript when providing examples. Three is insightfulness: Does the model’s response reveal something beyond the line segment’s obvious meaning in the transcript? We ask the teachers to evaluate on a 3-point scale (yes, somewhat, no). Optionally, the teacher may additionally provide a free text comment, if they want to elaborate their answer.
Figure 37 shows the human interface for evaluating the CLASS observation items, and Figure 38 for evaluating the MQI observation items.
Suggestions The teachers evaluate the model suggestions along four axes. One is relevance: Is the model’s response relevant to eliciting more student mathematical reasoning in the classroom? Two is faithfulness: Does the model’s response have the right interpretation of the events that occur in the classroom transcript? Similar to the previous research question, we evaluate along this dimension because the model sometimes can hallucinate or misinterpret the events in the transcript when providing suggestions. Three is actionability: Is the model’s suggestion something that the teacher can easily translate into practice for improving their
teaching or encouraging student mathematical reasoning? Finally, four is novelty: Is the model suggestion something that the teacher already does in the transcript? Similar to the previous section, we ask the teachers to evaluate on a 3-point scale (yes, somewhat, no).
Figure 39 shows the human interface for evaluating the model suggestions.

heading: C Additional results on quantitative scoring
text: We include the additional results on the the quantitative scoring task.
CLASS Figure 40 shows scatter plots of the model predicted scores vs. the human scores. It shows this across CLASS observation items and
prompting methods (DA, DA+, and RA). Figure 41 shows the same data, but compares the human and model predicted score distribution.
MQI Figure 42 shows scatter plots of the model predicted scores vs. the human scores. It shows this across MQI observation items and prompting methods (DA, DA+, and RA). Figure 43 shows the same data, but compares the human and model predicted score distribution.
C.1 Interrater Agreement We compute interrater agreement on the examples that both teachers rated (20%). Since our goal was to collect teachers’ unbiased perceptions, we did not conduct any calibration for this task; we leave this for future work. For task B, we measure a Cohen’s kappa with linear weighting of 0.16 for rele-
vance, 0.23 for faithfulness, and 0.32 for insightfulness. Figure 7 illustrates why there is particularly low agreement on relevance: One rater tends to select more extreme values for relevance, whereas the other rater selects more uniformly across the values. This results in low agreement for relevance. The Cohen’s kappas with quadratic weighting are 0.23 for relevance, 0.36 for faithfulness, and 0.37 for insightfulness. The Cohen’s kappas with quadratic weighting is slightly higher as it adjusts the penalty between scores 1 and 3 to be different from the penalty between scores 1 and 2 for instance. For Task C, we only have 2 examples per criterion, which is too sparse for computing Cohen’s kappa.

heading: D Examples of Transcripts, Model Responses, and Human Evaluations
text: Figure 44 shows a concrete example of the suggestions prompt given to the model. Figure ?? then shows one of the suggestions that the model generates. Figure 45 then shows the ratings provided from one of the human annotators on that suggestion.
CLPC CLBM CLINSTD
CLPC CLBM CLINSTD
EXPL REMED LANGIMP SMQR

heading: Future Work
text: Since our goal was to collect teachers’ unbiased perceptions, we did not conduct any calibration for this task; we leave this for future work. For task B, we measure a Cohen’s kappa with linear weighting of 0. 16 for rele-
vance, 0. 23 for faithfulness, and 0. 32 for insightfulness. Figure 7 illustrates why there is particularly low agreement on relevance: One rater tends to select more extreme values for relevance, whereas the other rater selects more uniformly across the values. This results in low agreement for relevance. The Cohen’s kappas with quadratic weighting are 0. 23 for relevance, 0. 36 for faithfulness, and 0. 37 for insightfulness. The Cohen’s kappas with quadratic weighting is slightly higher as it adjusts the penalty between scores 1 and 3 to be different from the penalty between scores 1 and 2 for instance. For Task C, we only have 2 examples per criterion, which is too sparse for computing Cohen’s kappa