Improving Hybrid Human-AI Tutoring By Differentiating The Human Tutor’s Role Based On Student Need Hybrid human-AI tutoring research highlights the promise of scaling personalized learning by combining adaptive AI tutor support with personalized instructional and motivational guidance from human tutors. However, emerging evidence suggests that human-AI tutoring is more beneficial for lower-performing students than higher-performing peers. This study evaluates a proactive-reactive personalization policy, where human tutors proactively initiate support for lower-performing students (below median), while higher-performing students receive reactive, on-demand support. Using a quasi-experimental difference-in-discontinuities design, 635 students (grades 5–8) were assigned to receive proactive or reactive tutoring during math practice using IXL (an AI tutor). Treatment assignment was determined using within-grade median state test scores. Results indicate that human-AI tutoring led to 43% (p = .01) growth on standardized tests compared to AI-only tutoring (i.e., 2.0× vs. 1.4× of expected growth). While the effects benefited both human-AI tutoring, the performance improvement was marginally greater than reactive tutoring (0.13 SD, p = .059). Beyond standardized tests, human-AI tutoring significantly increased students’ time-on-task (+1.38 hours, p < .001) and skill proficiency (+6.87 skills, p < .001) compared to AI-only tutoring. Finally, mediation analysis reveals a direct benefit of proactive tutoring on standardized test performance, despite lower time on task and skill proficiency compared to reactive tutoring—suggesting a “slow to go fast” dynamic where learning from direct interactions with tutors went beyond learning from IXL alone. Overall, the findings provide empirical evidence that access to human-AI tutoring leads to greater academic growth compared to AI-only tutoring, particularly for lower-performing students. More practically, these findings highlight a cost-effective strategy for scaling human-AI tutoring by allocating resources based on student needs, while improving learning for all students. Additional Keywords and Phrases Human-AI Tutoring, Learning Outcomes, Causal Inference

The Neglected 15%: Positive Effects Of Hybrid Human-AI Tutoring Among Students With Disabilities Abstract Incorporating human tutoring with AI holds promise for supporting diverse math learners. In the U.S., approximately 15% of students receive special education services, with limited previous research within AIED on the impact of AI-assisted learning among students with disabilities. Previous work combining human tutors and AI suggests that students with lower prior knowledge, such as lacking basic skills, exhibit greater learning gains compared to their more knowledgeable peers. Building upon this finding, we hypothesize that hybrid human-AI tutoring will have positive effects among students in inclusive settings, classrooms containing both students with and without identified disabilities. To investigate this hypothesis, we conduct a two-study quasi-experiment across two urban, low-income middle schools: one in Pennsylvania with 362 students and another in California comprised of 733 students, involving 27% and 16% of students with disabilities, respectively. Our findings indicate hybrid human-AI tutoring has positive effects on learning processes and outcomes among all students, including students with disabilities. The motivational benefits of the human tutoring treatment over a control group using solely math software show up in greater increases in practice and skill proficiency during math software use for students with disabilities than other students. In addition, the treatment yields significantly higher pre-post learning gains for all including students with disabilities whose relative gains trend higher and are statistically at least as high. These compelling findings support the promise of hybrid human-AI solutions and emphasize the collaborative focus among AIED to support diverse learners. Keywords Human-AI tutoring; Learning analytics; Equity; Inclusion.

Personalized Learning2 (PL2) is a professional mentoring platform created by researchers at Carnegie Mellon. Its goal is to improve workplace efficiency and utilize personalized learning to teach through situation-based instruction. PL2 combines both AI and mentor driven research training to help under-trained tutors with personalized learning. This platform includes social-emotional learning, math content and culturally responsive teaching practices to address the gap between historically marginalized students by training tutors to be more efficient and productive. PL2 offers a lower cost option for deliberate practice in order to increase impact and learning capacity of tutors.

This demonstration shows the recent advancement of scenario based tutor training and its focus on using the learn-by-doing approach. The 15 minute lessons outlined in this study use the predict-observe-explain inquiry method to develop tutor skills in helping student motivation. These methods are being developed within the Personalized Learning2 (PL2) program. PL2 is an app that combines student software with human tutors to improve mentoring ability. Enhancing mentor training will help to increase student ability while also maintaining low costs. This form of training works best when tutors have scenario based practice with response specific feedback.

This study discusses the development of Personalized Learning 2 (PL2), an online human mentoring system. PL2 uses student math learning data and mentor input to write custom feedback. This particular research is focused on finding a more efficient and research-based way to organize resources for PL2. 18 PL2 partner members completed a survey that revealed that Engaging and Motivating Students was the most important skill and Underingstanding Educational Norms and Policies was the least important. Reorganization will optimize mentor training and their ability to help students overcome barriers.

In a recent study, math problems in Carnegie Learning’s MATHia adaptive learning software were rewritten by human authors and AI to improve clarity. Findings showed that readers spent less time reading rewritten human content and achieved higher mastery than did readers who read the original content. The team conducting the study also used GPT-4 to rewrite the same set of math word problems with the same guidelines that the human authors used. comparing zero-shot, few-shot and chain-of-thought prompting strategies. Overall, report analysis of human-written, original and GTP-written problems showed that GTP rewrites have the most optimal readability, lexical diversity and cohesion scores, though used more low frequency words. Carnegie Learning plans to present their outputs at randomized field trials in MATHia.