2.4 Tuning In Sessions

2.4.1 A framework for balancing equity, pedagogy, and management of a multi-section course
Lindsey Shorser, Assistant Professor, Teaching Stream, Mathematics and Computer Science, FAS

Filtering the Noise: Tools, Trends, and Tensions

Building on the theme for this year's symposium, the signal to noise ratio of things a course coordinator *could* be doing and the things they *should* be doing can easily lead to burnout. In this talk, I will present a conceptual tool for focusing on one aspect of a course at a time, once perspective at a time, and one subset of a "to do" list at a time for anyone running an undergraduate course. The three-role framework for conceptualizing course coordination provides a way to cut through the noise, balance priorities, and improve intentionality as opposed to making reactive decisions.

The development of this three-role framework was the result of a systematic reflection on the tasks required to coordinate a first-year multi-section math course. The responsibilities of a course coordinator can be divided into three coherent roles, each with its own tasks, priorities, and motivations -- the Educator, the Communicator, and the Manager. The intention behind this conceptualization is to ensure pedagogically sound and equitable experiences for all students while effectively managing course resources.

In this talk, we will explore the framework's roles, the conflicting motivations of each, and the resulting benefits to time-management and intentional decision-making when using this framework.

Practice Track

2.4.2 Teaching Judgment Through Examples: Communication, Critical Thinking, and Civil Discourse in AI-Rich Learning Environments

William Ju, Professor, Teaching Stream, Cell and Systems Biology/Human Biology Program, FAS Julia Gallucci, Assistant Professor, Teaching Stream, FIS

Amplifying the Signal: Connection, Engagement, and Civil Discourse

In AI-rich learning environments, students regularly encounter fluent explanations and arguments, yet often struggle to evaluate their quality effectively. When expectations for reasoning and evidence are implicit, discussion can drift toward surface agreement, defensiveness, or disengagement, thus amplifying instructional noise rather than true learning.

This session shares a communication-focused teaching practice that uses short examples to teach judgment through discussion, positioning critical thinking and discourse as learnable skills. Implemented in large second- and third-year undergraduate courses, the practice centers on examining, comparing, and deconstructing brief responses (i.e. AI-generated, novice, as well as instructor-curated explanations) during lectures or tutorials. Rather than using AI for producing answers, students practice articulating what makes an explanation strong or weak, which criteria they are using, and how evidence supports claims.

Structured discussion prompts guide students to justify judgments, respond to alternative interpretations, and revise their thinking respectfully. These activities make disciplinary standards explicit and give students shared language for critique, supporting evidence-based, civil discourse even when disagreement is present. Artificial intelligence is treated as contextual rather than instructional: it serves as one source of examples, not as the sole tool that students should be using as a form of fluency/proficiency.

Early reflections suggest that teaching judgment through example-based discussion amplifies instructional signal by clarifying standards for reasoning, strengthening critical thinking, and improving the quality of academic dialogue without adding assignments or complexity.

Practice Track