As machine learning (ML) becomes integral in more disciplines, introductory courses in the field are attracting increasingly diverse audiences. Design of these introductory ML courses needs to be theoretically sound, but also intuitive, engaging, and accessible to a range of students. Effective teaching of ML must go beyond teaching the theoretical or practical mechanics of algorithms. In this paper, we synthesize effective teaching strategies from 6 experienced ML instructors across 5 institutions to help students define appropriate ML problems, build intuition, develop reasoning skills, and apply models responsibly. We organize these strategies into eight thematic areas: preparing students for success, motivating learners through real-world relevance, integrating ethics and societal impact, avoiding common methodological pitfalls in model evaluation, guiding students on design decisions, adapting effective classroom practices, assessing student learning, and preparing for the future. Each section offers practical examples of classroom-tested activities (or references to existing resources), and in many cases, reflections on our experiences with the strategies. Our aim is for this paper to be a starting point for instructors aiming to improve learning in introductory ML courses. We hope this is a resource-rich guide for teaching ML to diverse learners, grounded in both pedagogy and practice.

This paper presents a set of in-class activities and chemistry-based lab exercises developed for a CS1 course in Python that teaches programming through scientific contexts. The course pairs context-free in-class practice with a series of chemistry lab problems that increase in complexity, prompting students to apply computational thinking and form connections across course components. Survey results indicate that students responded positively to this approach.

Introductory computer science (CS1) courses are foundational to students' computing education, yet they often rely on abstract, decontextualized problems and introduce core concepts only once. This can lead to fragile learning, where students struggle to retain knowledge or apply it in new contexts. To address these challenges, we designed a CS1 course that introduces computing twice—first through a biology lens, then through a chemistry lens. This dual-introduction, dual-context structure aims to reinforce foundational computing concepts while highlighting their relevance across scientific domains. Our results indicate that the course structure effectively supported student learning, and end-of-semester survey responses reflected strong student engagement and appreciation for the interdisciplinary approach.

In an undergraduate CS1 course, we designed two projects to help students apply their learning: one in biology and one in chemistry. In this paper, we focus on the biology project, in which students identify and visualize gene mutations using real BRCA1 gene data. The projects received positive feedback, helping students see how coding applies to real-world problems in different scientific fields, understand real-world limitations, and reinforce their programming and problem-solving skills. This multi-context approach ensures a comprehensive understanding of programming concepts and their applications.

In an undergraduate CS1 course, we designed two projects to help students apply their learning: one in biology and one in chemistry. In this paper, we focus on the chemistry project, which centers on cheminformatics, the application of computational methods to analyze and interpret chemical data, which are particularly useful in drug discovery. In this project, students filter drug candidates using the PubChem database. The projects received positive feedback, helping students see how coding applies to real-world problems in different scientific fields, understand real-world limitations, and reinforce their programming and problem-solving skills. This multi-context approach ensures a comprehensive understanding of programming concepts and their applications.

One important aspect of effective human--robot collaborations is the ability for robots to adapt quickly to the needs of humans. While techniques like deep reinforcement learning have demonstrated success as sophisticated tools for learning robot policies, the fluency of human-robot collaborations is often limited by these policies' inability to integrate changes to a user's preferences for the task. To address these shortcomings, we propose a novel approach that can modify learned policies at execution time via symbolic if-this-then-that rules corresponding to a modular and superimposable set of low-level constraints on the robot's policy. These rules, which we call Transparent Matrix Overlays, function not only as succinct and explainable descriptions of the robot's current strategy but also as an interface by which a human collaborator can easily alter a robot's policy via verbal commands. We demonstrate the efficacy of this approach on a series of proof-of-concept cooking tasks performed in simulation and on a physical robot.

Robot-to-Human handovers are common exercises in many robotics application domains. The requirements of handovers may vary across these different domains. In this paper, we first devised a taxonomy to organize the diverse and sometimes contradictory requirements. Among these, task- oriented handovers were not well-studied but important because the purpose of the handovers in human–robot collaboration (HRC) is not merely to pass an object from a robot to a human receiver, but to enable the human receiver to use it in a subsequent tool-use task. A successful task-oriented handover should incorporate task-related information – orienting the tool such that the human can grasp it in a way that is suitable for the task. We identified multiple difficulty levels of task-oriented handovers, and implemented a system to generate task-oriented handovers with novel tools on a physical robot. Unlike previous studies on task-oriented handovers, we trained the robot with tool-use demonstrations rather than handover demonstrations, since task-oriented handovers are dependent on the tool usages in the subsequent task. We demonstrated that our method can adapt to all difficulty levels of task-oriented handovers, including tasks that matched the typical usage of the tool (level I), tasks that required an improvised and unusual usage of the tool (level II), and tasks where the handover was adapted to the pose of a manipulandum (level III). We evaluated the generated handovers with online surveys. Participants rated our handovers to appear more comfortable for the human receiver and more appropriate for subsequent tasks when compared with typical handovers from prior work.

Studies have shown that people conform their answers to match those of group members even when they believe the group’s answer to be wrong [2]. In this experiment, we test whether people conform to groups of robots and whether the robots cause informational conformity (believing the group to be correct), normative conformity (feeling peer pressure), or both. We conducted an experiment in which participants (N = 63) played a subjective game with three robots. We measured humans’ conformity to robots by how many times participants changed their preliminary answers to match the group of robots’ in their final answer. Participants in conditions that were given more information about the robots’ answers conformed significantly more than those who were given less, indicating that informational conformity is present. Participants in conditions where they were aware they were a minority in their answers conformed more than those who were unaware they were a minority. Additionally, they also report feeling more pressure to change their answers from the robots, and the amount of pressure they reported was correlated to the frequency they conformed, indicating normative conformity. Therefore, we conclude that robots can cause both informational and normative conformity in people.

While abstract knowledge like cause-and-effect relations enables robots to problem-solve in new environments, acquiring such knowledge remains out of reach for many traditional machine learning techniques. In this work, we introduce a method for a robot to learn an explicit model of cause-and-effect by constructing a structural causal model through a mix of observation and self-supervised experimentation, allowing a robot to reason from causes to effects and from effects to causes. We demonstrate our method on tool affordance learning tasks, where a humanoid robot must leverage its prior learning to utilize novel tools effectively. Our results suggest that after minimal training examples, our system can preferentially choose new tools based on the context, and can use these tools for goal-directed object manipulation.

We conducted a study on whether dogs (Canis familiaris) perceive robots as agents, using the classic pointing paradigm in animal cognition research. While few studies to date have explored the pointing paradigm with robots, an initial study did not suggest dogs understood non-humanoid robot pointing. In this study, we tested 20 dogs with the humanoid robot Nao. Our results did not suggest that dogs understand humanoid robot pointing. We are currently working on revising the design and will conduct more experiments.