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1. Undergraduate Computing Tutors' Perceptions of their Roles, Stressors, and Barriers to Effectiveness

   https://doi.org/10.1145/3641554.3701784  

   Villegas_Molina, Ismael; Kim, Jeannie; Montalvo, Audria; Larragoitia, Apollo; Lim, Rachel S; Guo, Philip J; Krause-Levy, Sophia; Porter, Leo (February 2025, ACM)

   Undergraduate teaching assistants (tutors) are commonly employed in computing courses to help students with programming assignments. Prior research in computing education has reported the benefits of tutoring both for students and for the tutors' own learning. In contrast, recent research that examined actual tutoring sessions has reported that these sessions may be less productive than one might hope, with tutors often just giving students the answers to their problems without trying to teach the underlying concepts. To better understand why tutors may be employing these suboptimal practices, we interviewed ten tutors across early computing courses in higher education to identify their perceived role in these sessions, what stressors and factors influence their ability to perform their job effectively, and what kinds of best practices they learned in their tutor training course. Tutors reported their roles around student learning, gauging student understanding, identifying or providing solutions to students, and providing socioemotional support. They reported their stressors around environmental factors (e.g., number of students waiting to be helped, preparation time, peer-tutor frustrations), internal influences, student behavior, student skill levels, and feeling the need to ''read a student's mind.'' Regarding their tutor training course, Tutors reported learning about interaction guidelines and procedures and question-based problem solving. We conclude by discussing how these results may contribute to the less-effective behaviors seen in prior research and potential ways to improve tutoring in computing courses. 

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2. Leveraging LLM Tutoring Systems for Non-Native English Speakers in Introductory CS Courses

   Villegas_Molina, Ismael; Nikitza_Montalvo, Audria; Ochoa, Benjamin; Denny, Paul; Porter, Leo (June 2025, 2025 ASEE Annual Conference & Exposition)

   Computer science has historically presented barriers for non-native English speaking (NNES) students, often due to language and terminology challenges. With the rise of large language models (LLMs), there is potential to leverage this technology to support NNES students more effectively. Recent implementations of LLMs as tutors in classrooms have shown promising results. In this study, we deployed an LLM tutor in an accelerated introductory computing course to evaluate its effectiveness specifically for NNES students. Key insights for LLM tutor use are as follows: NNES students signed up for the LLM tutor at a similar rate to native English speakers (NES); NNES students used the system at a lower rate than NES students---to a small effect; NNES students asked significantly more questions in languages other than English compared to NES students, with many of the questions being multilingual by incorporating English programming keywords. Results for views of the LLM tutor are as follows: both NNES and NES students appreciated the LLM tutor for its accessibility, conversational style, and the guardrails put in place to guide users to answers rather than directly providing solutions; NNES students highlighted its approachability as they did not need to communicate in perfect English; NNES students rated help-seeking preferences of online resources higher than NES students; Many NNES students were unfamiliar with computing terminology in their native languages. These results suggest that LLM tutors can be a valuable resource for NNES students in computing, providing tailored support that enhances their learning experience and overcomes language barriers. 

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3. Designing for complementarity: Teacher and student needs for orchestration support in AI-enhanced classrooms

   https://doi.org/10.1007/978-3-030-23204-7_14  

   Holstein, Kenneth; McLaren, Bruce M.; Aleven, Vincent (June 2019, Proceedings, 20th International Conference on Artificial Intelligence in Education, AIED 2019)

   As artificial intelligence (AI) increasingly enters K-12 classrooms, what do teachers and students see as the roles of human versus AI instruction, and how might educational AI (AIED) systems best be designed to support these complementary roles? We explore these questions through participatory design and needs validation studies with K-12 teachers and students. Using human-centered design methods rarely employed in AIED research, this work builds on prior findings to contribute: (1) an analysis of teacher and student feedback on 24 design concepts for systems that integrate human and AI instruction; and (2) participatory speed dating (PSD): a new variant of the speed dating design method, involving iterative concept generation and evaluation with multiple stakeholders. Using PSD, we found that teachers desire greater real-time support from AI tutors in identifying when students need human help, in evaluating the impacts of their own help-giving, and in managing student motivation. Meanwhile, students desire better mechanisms to signal help-need during class without losing face to peers, to receive emotional support from human rather than AI tutors, and to have greater agency over how their personal analytics are used. This work provides tools and insights to guide the design of more effective human–AI partnerships for K-12 education. 

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4. An Exploration of Student-Tutor Interactions in Computing

   https://doi.org/10.1145/3502718.3524786  

   Krause-Levy, Sophia; Lim, Rachel S.; Villegas Molina, Ismael; Cao, Yingjun; Porter, Leo (July 2022, Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education)

   As enrollments in computing courses have surged, the ratio of students to faculty has risen at many institutions. Along with many other large undergraduate programs, our institution has adapted to this challenge by hiring increasing numbers of undergraduate tutors to help students. In early computing courses, their role at our institution is primarily to help students with their programming assignments. Despite our institution offering a training course for tutors, we are concerned about the quality and nature of these student-tutor interactions. As instruction moved online due to COVID-19, this provided the unique opportunity to record all student-tutor interactions (among consenting participants) for research. In order to gain an understanding of the behaviors common in these interactions, we conducted an initial qualitative analysis using open coding followed by a quantitative analysis on those codes. Overall, we found that students are not generally receiving the instruction we might hope or expect from these sessions. Notably, tutors often simply give students the solution to the problem in their code without teaching them about the process of finding and correcting their own errors. These findings highlight the importance of tutoring sessions for learning in introductory courses and motivate remediation to make these sessions more productive. 

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5. Using Trained Tutors to Improve Mechanical Engineering Technology Student Writing

   Clippinger, D.; Pflueger, R. C.; Nozaki, S.; Bodenhamer, J. (June 2023, American Society for Engineering Education)

   In an earlier work, the authors compared the writing style of Mechanical Engineering Technology (MET) students in an “untutored” state to the writing style of “tutored” students, where the tutoring was provided by “generic” writing center tutors. The results of this study showed that aside from changes in the diction of the students’ work, there was little measurable improvement in the quality of student writing as measured by both the AAC&U VALUE Rubric and by the authors’ voice-development-style-diction method. The current work builds on the results of the previous work by providing training on a just-in-time basis for the writing center tutors. As with previous years, the students participating in the study were MET students in a last-semester capstone industrial design course. This course is organized around a series of open-ended industry-sponsored projects for which the students are expected to develop a solution to a mechanical engineering problem. The students work on the projects in teams of three or four students and complete the work over a two-semester sequence offered annually on a fall-spring basis. The assignment in the study was identical to that of previous years: an “analysis” report in which students are expected to apply content from previous courses to one aspect of the industry-sponsored design project. The present study will compare the results from three iterations of the study: the work of “untutored” students, i.e. those who did not received any writing center assistance whatsoever, those who tutored by “generic” writing center tutors, and lastly, the works of those tutored by tutors specifically trained in support of the specific intervention. In the two cases where tutor interaction occurred, it was required as a component of the course to ensure participation by the entire student cohort. In general, the interactions with the specially-trained tutors produced works with a more mature writing style on the part of the student as compared to those works produced by students who had interacted with the untrained tutors or no tutors at all. The work will also discuss survey data collected on the “generic” and specially-trained tutoring sessions and discuss the differences in the results. Preliminary results show that the specially-trained tutors reported pronounced levels of engagement in the tutoring session, as measured by student note-taking, student questions, student receptiveness to suggestions, and student desire to understand the reasoning behind the tutors’ suggestions. Specially-trained tutors also reported significantly higher levels of student interest suggestions about grammar, style, content, format, and citations. Overall, it is concluded that specific training for the tutors was most associated with increased levels of interaction between tutor and student. As the students in the final group (“trained tutors”) were told prior to the tutoring session that the tutors were “specially trained,” it remains to be determined if the increased interaction was due to better tutor preparation or a higher estimation of the value of the tutoring session on the part of the students receiving the tutoring. This is proposed as an extension to the current work. 

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