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Research indicates that peer mentoring is an effective solution for building ethnic and racial minority women’s self-efficacy to persist in STEM degrees and careers. What is not known, however, is how to design a training program grounded in learning theory to build mentoring knowledge, skills, and self-efficacy in underrepresented populations in Historically Black Colleges and Universities (HBCU). This instructional design case fulfills that gap by presenting details of design challenges and decisions made while developing an eight-week online training program for both mentors and mentees participating in a STEM peer mentoring program at two HBCUs. This design case is a second iteration of the training program following an initial pilot study. Stakeholder feedback, along with a conceptual framework, including Tinto’s Institutional Departure Model and Bandura’s Theory of Self-Efficacy, guided the design work. 

Learners often have metacognitive deficits that limit their ability to select material at appropriate levels in independent studying situations. The increasing prevalence of intelligent recommender systems can assume this role, while also fostering a kind of experiential meta-instruction. The creation of hybrid tutors (federated systems of both adaptive and static learning resources with a single interface and learning record store) provides an opportunity to test this experiential instruction of metacognitive strategies. As a test case, we examine the hybrid tutor ElectronixTutor, which has two distinct intelligent recommender engines corresponding to distinct use cases. Each of these constitutes a method of providing scaffolding to learners so that they can internalize the principled, theoretically informed reasons for the order of their progression through learning content. However, the learning described is speculative and requires evaluation. By examining expected efficacy, perceived efficacy, actual efficacy, and especially the relationships among these three concepts, actionable insights should arise pertaining to adaptive instructional system design, learning science generally, and other areas