After the Answer Designing Attention in the Automated Academy
What is the university for when the tutor is a 24-hour service? What in a professor does a student actually attend to, and how does a professor attend to a student?
Universities face a false binary: attack the problem of generative AI head on, or adopt it uncritically. Quick to respond to the growing crisis, some institutions have imposed detection software, integrity task forces, and syllabus language drafted by committees. Others, under pressure to cut costs or from fears of falling behind, have partnered with AI companies to automate the parts of education where human interaction matters the most: advising, feedback, tutoring, and the seminar itself. Dogged by fears of cheating, students and faculty hide uses of generative AI, preventing any open dialogue about the issue. Both institutional responses are automatic top-down solutions, and both overlook how knowledge actually evolves organically, among minds, bodies, and the systems they build together.
The problem is not the machine but the machine as a model of cybernetic efficiency. Industrial models of education treat the learning process as a feedback system, with measured outputs, standardization, and cost-effectiveness. Who will design the post-industrial university, and who will control its infrastructure and spaces of attention? Whose labor will it replace, and who will be left out of its promised efficiencies?
Issue 06 asks contributors to imagine alternatives for the academy engaged with AI systems. Stripped down to essentials, the university promises the attention of faculty, requires the attention of students, and depends on the sustained attention of researchers. These roles can now be automated. Handed over to powerful AI systems focused entirely on individual needs, the attention of student and faculty might become locked into a recursive subjective space that simulates an expansion of knowledge but leaves out vital connections with others who think differently. A university in its lived experience is also an assemblage of labs, protocols, machines, hallways, archives, practices, catalogs, and gathering spaces. Artificial intelligence has quietly inserted itself into this assemblage, mostly without design and under the radar. The university emerged within particular material conditions over centuries, and can be redesigned or replaced within the new conditions of automation. What will be left for humans to attend to? Where will attentive learning and teaching actually take place, and what is being offloaded in its place?
Research has its own practices of attention, and AI systems now enter every stage of the process, from searching and summarizing documents to drafting grant proposals and reviewing manuscripts. On the surface, these tools promise to accelerate the output of faculty who are under the pressure of publish-or-perish. But if papers are increasingly written and reviewed by machines, is anyone still reading? Is the advisor still attending to the graduate student’s growth? If AI opens new forms of inquiry, new discoveries, what kind of attention is left in its wake?
A more organic approach to automation might simply lie in deliberate tinkering with AI at all levels of the industrial education model: in the classroom, the lab, the essay, the exam, and the grade. How can we design an AI that itself attends to the full complexity of human learning and teaching?
Art and design education offers one model for thinking beyond the industrial university, where teacher and student attend to each other through making. The arts teach through iteration, resistance of materials, affordances of instruments, and unpredictable encounters. Can AI systems extend this model into other learning spaces? What can other fields learn from pedagogy focused on creative, even speculative research? And what do the arts lose if the studio and critique are streamlined and automated like everything else?
When automation follows industrial logic, it strips away originality of thought and inquiry instead of valuing the lived learning experience of each individual. Following a different logic, automation might take on much of the individualized instruction and tutoring, freeing the classroom for more human interaction, not less. How far could the classroom be flipped? Instead of treating each student as the same site of input and output, the academy might renew practices that mass education has often marginalized: apprenticeship, debate, oral examination, creative research, fieldwork, the long philosophical walk.
We are looking for works that recover what has been lost, experiment with new forms of learning and interaction, or expose how automation directs, measures, and confines our attention. What new encounters might these technologies make possible? What forms of difference might they quietly exclude?
We want to hear from scholars, artists, students, staff, administrators, and from those who left the academy or never entered it.
Collaborative, cross-institutional, and non-institutional work is welcome.
Form
Work must be born-digital, taking multimodal, interactive, computational, audiovisual, or other browser-accessible forms.
Projects might include:
- simulated agents and tutoring systems
- speculative educational interfaces
- invented archives, counterfeit institutional records
- networked or collectively authored projects
- interactive and nonlinear essays
- speculative college promotional videos
- learning games and playable systems
- generative, procedural, and computational literature
- virtual campuses and 3D learning environments
- speculative syllabi, abstracts, grant proposals, peer reviews, rubrics, exams, and other academic artifacts
- netprov classrooms or student forum discussions
We are particularly looking for works in which form itself becomes a method of inquiry. We welcome humor, satire, parody, and the absurd.
Artificial intelligence is welcome as subject, medium, collaborator, and antagonist. If a work was made in collaboration with AI, we are interested in the documentation of that collaboration.
How to Submit
Submit a proposal of no more than 500 words through the Issue 06 submission form by January 15, 2027. Describe the proposed work’s form and how it engages the issue’s themes.
Proposals for new works are encouraged; projects do not have to be completed at the time of submission.
- Proposals due January 15, 2027
- Notification of acceptance February 15, 2027
- Completed works due July 30, 2027
- Expected publication September 15, 2027
For all other inquiries, please email: wluers@wsu.edu.