An explosion in automated machine learning (AutoML) tools has led to numerous back-end frameworks enabling users with machine learning expertise to leverage the power of Machine Learning (ML). However, to ensure that ML tools are openly accessible to all who stand to benefit from their predictive and analytical powers, we must examine how true novices without ML knowledge interact with AutoML tools, perceive ML, and form their mental models of ML processes. We achieve this goal with our user-facing framework that combines the understandability of conversation with Large Language Models (LLMs) and the interface scaffolding necessary to support true machine learning novices in building their own models. We then evaluate the effectiveness of our framework in a user study. Results show that our ML-novice participants felt confident performing ML tasks independently, citing the tool’s ease of use and its ability to help them formalize their ML goals.

As machine intelligence is increasingly incorporated into educational technologies, it becomes imperative for instructors and students to understand the potential flaws of the algorithms on which their systems rely. This paper describes the design and implementation of an interactive post-hoc explanation of the Bayesian Knowledge Tracing algorithm which is implemented in learning analytics systems used across the United States. After a user-centered design process to smooth out interaction design difficulties, we ran a controlled experiment to evaluate whether the interactive or static version of the explainable led to increased learning. Our results reveal that learning about an algorithm through an explainable depends on users' educational background. For other contexts, designers of post-hoc explainables must consider their users' educational background to best determine how to empower more informed decision-making with AI-enhanced systems.

This Workshop is intended to be a generative space for participants to create a module on ethics in a Computer Science course of their choice. Participants will choose an existing course in the typical CS curriculum; and coalesce around common themes to create a module that integrates critical reflection on ethical choices and societal impact of computing with the practice of computing. The workshop will offer a space to collaboratively work on designing an ethics module for a CS course chosen by the participants. The ethics module would integrate teaching specific concepts and practices in computing alongside a critical reflection of these practices. Ideally, these modules would be flexible enough to fit into curricula at various institutions.

Learning analytics systems have the potential to bring enormous value to online education. Unfortunately, many instructors and platforms do not adequately leverage learning analytics in their courses today. In this paper, we report on the value of these systems from the perspective of course instructors. We study these ideas through OARS, a modular and real-time learning analytics system that we deployed across more than ten online courses with tens of thousands of learners. We leverage this system as a starting point for semi-structured interviews with a diverse set of instructors. Our study suggests new design goals for learning analytics systems, the importance of real-time analytics to many instructors, and the value of flexibility in data selection and aggregation for an instructor when working with an analytics system.