AI as – Online teaching can silently expand faculty workload—more posts, more feedback, more constant availability. A new argument in higher-ed practice says AI should not just speed up tasks, but help faculty design sustainable weekly workflows through structured ref
When the workday never really ends, it doesn’t always feel like overload at first. It can look normal: another discussion board thread to read. another draft to refine. another student email that lands after hours. In online teaching environments especially. faculty workload can expand quietly and persistently—until boundaries erode and reflective practice gets pushed out by reactive performance.
Artificial intelligence has often arrived in these spaces as a productivity tool: something that can draft announcements. summarize readings. or generate quiz questions. The thrust here is different. Used intentionally. AI can function as a structured reflective partner—helping faculty visualize. model. and design sustainable workflows. rather than accelerating academic labor.
This matters because the expansion is not abstract. The contemporary faculty workload is described as both visible and invisible. Visible are courses, syllabi, scheduled advising hours, and committee meetings. Invisible are facilitation in discussions. emotional labor in student emails. feedback that stretches late into the evening. and the cognitive fragmentation caused by digital availability. Over time, the “steady hum of obligation” becomes diffusion: attention scattered across roles without structural containment.
Studies of online faculty workload have documented expanded time demands and blurred boundaries compared to face-to-face instruction (Van de Vord & Pogue. 2012; Conceição & Lehman. 2011). The piece’s concern is not that faculty effort is inherently inefficient. but that without workload being mapped in concrete terms. responsibilities expand toward perfectionistic over-functioning—where caring for students turns into an unsustainable self-demand. Faculty who want to keep students engaged may over-perform in discussion boards. provide extensive written feedback. and remain constantly available through their inbox. The argument is blunt: these practices are rarely sustainable across a 15-week semester.
The proposed shift starts with how AI is prompted. Instead of asking AI to draft materials, faculty can ask it to model their workload. One example prompt lays out a realistic staffing and life situation: “Develop a sustainable weekly workflow plan for three 3-credit online courses with 40 students each. five advising hours. two committee obligations. and caregiving responsibilities for a busy household of four. Organize by cognitive intensity, include grading containment strategies, and build in burnout prevention checkpoints.”.
The power of the prompt is not limited to the output. To write such a prompt. faculty must quantify their teaching load. name service commitments. and acknowledge personal responsibilities—making invisible labor visible. The workflow AI returns then becomes a starting point for a second stage of reflection and evaluation. where faculty are prompted to ask whether the plan assumes unlimited energy. where boundaries are explicit. whether grading tasks are batched. whether advising is emotionally contained rather than scattered. whether protected deep-work blocks exist. and whether there is a true day off.
The message is clear: the goal is not to adopt an AI-produced schedule uncritically. The goal is to use AI like a design prototype—something that models reality and invites revision, reiteration, and recalibration. In that approach, AI becomes a mirror rather than a manager.
A central method for these models is organizing tasks by cognitive intensity rather than by time. High-intensity work is described as grading essays, providing individualized feedback, and preparing complex instructional materials. Moderate-intensity work includes discussion facilitation, advising meetings, and committee contributions. Lower-intensity work covers email triage, administrative documentation, and course announcements.
Clustering high-intensity tasks into protected blocks earlier in the week is presented as a way to reduce cognitive fragmentation. Batching grading into two dedicated sessions rather than grading sporadically every evening, the argument says, preserves mental clarity. Containing advising into structured windows is framed as a way to prevent emotional spillover into unrelated tasks. AI, in this view, helps surface distinctions based on energy patterns rather than traditional 9-5 assumptions.
There is also a “good enough” threshold built into the idea of sustainability culture. Reflective practice is described through Schön’s (1992) definition—structured opportunities to step back from action in order to examine it. In online courses, discussion participation can become a site of overextension if faculty feel compelled to respond to every student. Research on instructor presence suggests strategic facilitation—clarifying early. probing midweek. and synthesizing at the end—can be equally effective without constant posting (Martin. Wang. & Sadaf. 2018).
Grading, too, can expand infinitely if there are no containment strategies. The piece lists options such as detailed rubrics, comment banks, audio feedback, and staggered due dates across sections. AI-generated workflow models are described as often including stopping rules like closing the laptop at a set time. designating one weekend day fully offline. and capping email checks to specific intervals. These suggestions may appear basic. but they are positioned as permission structures when faculty otherwise know the strategies yet lack operational reinforcement to enact them.
There is a recurring ethical boundary throughout the argument. Faculty should not input identifiable student information or sensitive advising details. Institutional expectations, union contracts, and workload policies must inform any workflow plan. AI outputs may reflect generalized assumptions that require contextual adjustment. and AI cannot assess the cultural or emotional nuance of individual departments or institutions. The technology is framed as scaffolding while educator authority remains central.
That includes resisting a familiar narrative: that AI should help faculty “do more.” If a workflow model suggests filling every available hour, it should be revised. Success is described not as maximized output but as sustained presence.
Work-life balance is treated here as pedagogical integrity, not only personal wellness. Faculty who are chronically depleted. the argument notes. struggle to offer thoughtful feedback. nuanced facilitation. and emotionally attuned advising (Cruz & Javier. 2023; Slavova & Tarpomanova. 2025). Conversely, protecting cognitive space supports more intentional instructional presence and emotional regulation. Sustainable workflows improve clarity, clarity improves presence, and presence improves learning environments. When faculty design semesters with containment in mind—staggering major assignments. batching grading. structuring advising. and protecting weekends—they are also modeling professional self-regulation for students. Adult learners in particular benefit from boundaries being enacted rather than preached.
The broader conversation about AI in higher education, as framed in this piece, often swings between excitement and alarm. What is offered instead is a quieter use: prompting AI to generate reflection about faculty labor patterns. Turning a workload prompt into a pause, quantify, and redesign process (Sarkar, 2026) surfaces hidden assumptions about availability, perfectionism, and overperformance. It encourages time to be treated as an ecosystem rather than a resource to be exhausted.
The writer argues that mitigating academic labor does not diminish rigor—it protects it. As institutions expand online offerings and faculty responsibilities continue to grow, humane workflow design is presented as increasingly urgent. In the closing vision. AI used critically and reflectively can serve as a scaffold—not to accelerate work indefinitely. but to contain it within boundaries that preserve intellectual and emotional vitality.
The piece ties that back to professional sovereignty: faculty who approach their work reflectively rather than reactively are better positioned to model balance. ethical decision-making. and intellectual clarity. AI will not solve academic overload by itself. but used thoughtfully it can act as a cognitive companion—helping instructors plan. prioritize. and protect the relational core of teaching.
Crystal Donlan. MEd. DEd(c). is identified as the Non-Credit Instructional Designer for Penn State World Campus and a faculty member and doctoral candidate in Lifelong Learning and Adult Education. Her scholarship is described as centering modern literacies. reflective practice. online and distance learning. and the ethical integration of AI in higher education. Her work is also said to have led her to develop best practice frameworks supporting inclusive, multimodal, learner-centered environments.
The piece references Conceição, S. C. O., & Lehman, R. M. (2011), Cruz, A. M., & Javier, R. D. (2023), Martin, F., Wang, C., & Sadaf, A. (2018), Sarkar, A. (2026), Schön, D.A. (1992), Slavova, V., & Tarpomanova, T. (2025), and Van de Vord, R., & Pogue, K. (2012).
AI in education faculty workload online teaching reflective practice sustainable workflows cognitive intensity grading containment work-life balance instructor presence higher education policy
So basically AI is gonna make teachers work forever? Cool cool.
I don’t get it… if AI can help design workflows then why are we still talking about boundaries breaking down. Sounds like more work either way, just with extra steps.
They say AI shouldn’t speed things up, but drafting announcements and quizzes is literally speeding it up. Also student emails after hours aren’t gonna stop because some app has “structured ref” or whatever.
This reads like a HR pitch like “don’t worry, AI will protect your time” but I’ve seen systems like this before and it always turns into more monitoring and more posts. Like if faculty use it to “visualize” their workload then admin will just see the numbers and ask for more. The invisible workload thing is real though, but I feel like AI is still part of the problem.