Today’s faculty workload includes both apparent and hidden elements. Visible tasks encompass courses, syllabi, scheduled advising, and committee meetings. Hidden tasks involve facilitating discussions, managing student emails, providing late-night feedback, and dealing with cognitive fragmentation due to digital availability. In online teaching, work quietly and continuously expands, leading to blurred boundaries. This often results in a shift from reflective practice to reactive performance.
Artificial intelligence (AI) is often introduced in academia as a tool for productivity—drafting announcements, summarizing readings, or creating quiz questions. Though useful, these applications overlook a more profound potential: AI can serve as a reflective partner, aiding faculty in visualizing, modeling, and designing sustainable workflows. When used deliberately, AI can contain academic labor rather than accelerate it.
Online teaching presents unique challenges, with faculty often managing multiple sections with large enrollments, along with advising, committee work, research, and personal obligations. The cognitive load is substantial, and studies show that online faculty face increased time demands and blurred boundaries compared to in-person instruction. Despite this, workloads are rarely concretely mapped, resulting in an ongoing sense of obligation.
Reflective practice is essential for sustainable academic workflow design. As Schön (1992) suggests, it requires opportunities to step back and examine actions. Faculty can use AI as reflective partners, not replacements, to support intentionality and maintain professional judgment. A simple exercise involves prompting AI to model a sustainable workload, making invisible labor visible and transforming vague overwhelm into structured design.
When AI proposes a workflow, faculty engage in reflection: evaluating energy assumptions, explicit boundaries, batching of tasks, and protected work blocks. The goal is not to adopt AI’s output blindly but to use it as a prototype for reality, inviting revisions. AI thus acts as a mirror, not a manager.
Organizing tasks by cognitive intensity rather than time can aid workflow modeling. High-intensity tasks like grading and preparing materials are best clustered earlier in the week, reducing cognitive fragmentation. AI can assist by suggesting workflow structures based on energy patterns, recognizing that faculty have cognitive limits and need to preserve teaching quality.
Reflective practitioners must balance improvement with sustainability. In online courses, strategic facilitation can be as effective as constant participation. Without strategies like rubrics or staggered due dates, grading can overwhelm weekends. AI-generated workflows often include stopping rules, embedding boundary-setting into the design process to foster a sustainability culture.
Using AI requires careful boundaries, avoiding identifiable student data or sensitive details. Institutional expectations and policies must guide workflow plans, and AI outputs may need contextual adjustments. While AI provides scaffolding, educators retain authority, resisting narratives that AI should increase faculty workload.
Original Source: facultyfocus.com
