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Artificial intelligence (AI) applications to support human tutoring have potential to significantly improve learning outcomes, but engagement issues persist, especially among students from low-income backgrounds. We introduce an AI-assisted tutoring model that combines human and AI tutoring and hypothesize this synergy will have positive impacts on learning processes. To investigate this hypothesis, we conduct a three-study quasi-experiment across three urban and low-income middle schools: 1) 125 students in a Pennsylvania school; 2) 385 students (50% Latinx) in a California school, and 3) 75 students (100% Black) in a Pennsylvania charter school, all implementing analogous tutoring models. We compare learning analytics of students engaged in human-AI tutoring compared to students using math software only. We find human-AI tutoring has positive effects, particularly in student’s proficiency and usage, with evidence suggesting lower achieving students may benefit more compared to higher achieving students. We illustrate the use of quasi-experimental methods adapted to the particulars of different schools and data-availability contexts so as to achieve the rapid data-driven iteration needed to guide an inspired creation into effective innovation. Future work focuses on improving the tutor dashboard and optimizing tutor-student ratios, while maintaining annual costs per student of approximately $700 annually.

Addressing negative self-talk by students, such as responding to a student when saying, "I am dumb"or "I can't do this"can be difficult for even the most experienced tutor. Despite potential tutor learning from scenario-based lessons on this topic, human-graded assessment remains time-consuming. Leveraging generative AI for evaluating textual responses in online training presents a scalable solution. Research suggests a tutor validates student's feelings when they speak negatively of themselves, e.g., by a tutor responding, "I understand how you feel"or "I recognize this is difficult."This ongoing work assesses the performance of 60 undergraduate tutors within an online lesson on enhancing tutors' abilities to respond to students engaging in negative self-talk. We find statistically significant tutor learning gains from pretest to posttest. Additionally, we describe a method of using generative AI for assessing tutors' responses to predict the best approach and subsequently explain the rationale behind it. Using the large language model GPT-4, we find high absolute performance when evaluating tutor responses involving predicting (F1 = 0.85) and explaining (F1 = 0.83) the best approach. Minor improvements are needed to the lesson itself. A future goal of this work is to fully develop automated systems of assessing tutor learning attending to barriers to students' motivation and doing so at scale.

Tutoring is among the most impactful educational influences on student achievement, with perhaps the greatest promise of combating student learning loss. Due to its high impact, organizations are rapidly developing tutoring programs and discovering a common problem- a shortage of qualified, experienced tutors. This mixed methods investigation focuses on the impact of short (∼15 min.), online lessons in which tutors participate in situational judgment tests based on everyday tutoring scenarios. We developed three lessons on strategies for supporting student self-efficacy and motivation and tested them with 80 tutors from a national, online tutoring organization. Using a mixed-effects logistic regression model, we found a statistically significant learning effect indicating tutors performed about 20% higher post-instruction than pre-instruction (β = 0.811, p < 0.01). Tutors scored ∼30% better on selected compared to constructed responses at posttest with evidence that tutors are learning from selected-response questions alone. Learning analytics and qualitative feedback suggest future design modifications for larger scale deployment, such as creating more authentically challenging selected-response options, capturing common misconceptions using learnersourced data, and varying modalities of scenario delivery with the aim of maintaining learning gains while reducing time and effort for tutor participants and trainers.

In this demonstration, we showcase the recent advancement of scenario-based tutor training with a focus to scale by applying the learn-by-doing approach to teaching strategies to provide socio-motivational support. These short (~15 min.) self-paced lessons use the predict-observe-explain inquiry method to develop mentor capacity in bolstering student motivation (i.e., fostering growth mindset). These custom training modules are being created to provide supplemental mentor support within the Personalized Learning2 system, an app which combines human tutoring and student math software to improve mentoring efficiency by connecting mentors to personalized resources, such as scenario-based mentor lessons, based on individual needs. Enhancing mentor training will aid in better quality mentoring at low cost. Mentor training is most effective when scenario-based practice provides trainees with response-specific feedback. To achieve feedback at scale, we illustrate an iterative design effort toward creating selected-response tasks that maintain some of the authenticity benefits of constructed-response. These scenario-based mentor lessons will be used by national level mentoring organizations as part of our efforts to scale.