  In this work, we aim to improve transparency and efficacy in human-robot
collaboration by developing machine teaching algorithms suitable for groups
with varied learning capabilities. While previous approaches focused on
tailored approaches for teaching individuals, our method teaches teams with
various compositions of diverse learners using team belief representations to
address personalization challenges within groups. We investigate various group
teaching strategies, such as focusing on individual beliefs or the group's
collective beliefs, and assess their impact on learning robot policies for
different team compositions. Our findings reveal that team belief strategies
yield less variation in learning duration and better accommodate diverse teams
compared to individual belief strategies, suggesting their suitability in
mixed-proficiency settings with limited resources. Conversely, individual
belief strategies provide a more uniform knowledge level, particularly
effective for homogeneously inexperienced groups. Our study indicates that the
teaching strategy's efficacy is significantly influenced by team composition
and learner proficiency, highlighting the importance of real-time assessment of
learner proficiency and adapting teaching approaches based on learner
proficiency for optimal teaching outcomes.
