Chapter 13
AI and Automation at Work, Home, and School
Abstract. [One short paragraph summarizing the chapter's scope and takeaways. Draft focus: how AI and automation reshape everyday settings—workplaces, domestic life, and education—and what that implies for human–AI interaction design, trust, and responsibility.]
13.1 Introduction
[Frame the chapter: AI/automation is no longer confined to specialized industrial or research settings. Sketch why work, home, and school are useful lenses (roles, stakes, oversight, skill development). Preview the chapter organization and point to related handbook chapters as needed (e.g., trust, explainability, teaming, ethics).]
Cognitive offloading –Coactive design Cognitive offloading -automation bias Cognitive surrender – cognitive surrender
From Woods: Supervisory control Cognitive Vacuum Law
Nobody is autonomous
research on NSF grant applications. Jagged Technological Frontier
AI-induced "cognitive decline"
Some of the studies I've worked through: Gerlich 2025 (666 participants, mixed methods) - Critical thinking score decline correlated with frequent AI use. Younger participants (17-25) showed steepest decline and highest dependence. Higher education served as a protective buffer but only delayed, didn't prevent, the decline. Yale 2025 (longitudinal, 4.5M adults over 10 years) - Self-reported cognitive disability in adults 18-34 went from 5.1
The closest analogues I can find are language learning (shown to improve cognitive flexibility) and certain forms of dialectical thinking practice. Neither is a daily practice at the right friction. Has anyone seen better candidates?
13.2 AI and Automation at Work
[Workplace applications: decision support, scheduling, monitoring, cobots, knowledge work assistants, hiring/HR tools, safety-critical ops, etc.]
13.2.1 Tasks, roles, and redistribution of labor
From https://x.com/mikejulian/status/2096450476170694785
Shifting bottlenecks
We ditched code review at @DuckbillHQ (mostly) About a month ago, we found ourselves with 60 open PRs for a team of five. They had been accumulating for a few weeks and we all had the sudden realization we were looking at two days of just code review. 12:08 AM · Sep 6, 2026 I had been tossing around the idea for a while about having AI do all code review and so I just asked the team: what if we just...didn't review the PRs? Mike Julian We decided to do a couple things instead: - Switch to a risk-based system - Improve our guardrails (unit and e2e testing, post-deploy o11y, stricter linting and type checking, etc) With a risk-based system, we agreed that if your change touched the public API/MCP, auth, design system, non-additive database schema changes, or agent skills, it needed a human review. We then enforced that with a shell script to add a github label. Improving guardrails was pretty easy, just expensive in tokens and attention. We enabled nearly every rule in ruff/prettier/eslint/ty and we improved our unit test coverage to a floor of 85%. We took a pretty high-level approach to o11y, preferring to instrument the customer-facing signals that indicate a bad time is about to happen (eg, ingestion, data processing, response times, auth). There's a few areas we went deeper on as needed, of course. We also spent a bunch of time rewriting our agent skills to ensure we were giving our agents better instructions. We had a lot of cruft from 2025-era AI. We wrote evals for our skills then tested them to see which had been consumed by modern LLM knowledge. We ultimately deleted a lot and then improved what remained. While we were there, we found a lot of markdown docs had been accumulating from doc-happy agents and leading to context poisoning We're now centralizing our docs into a single docs folder and requiring those be written by humans. Location gets enforced by another shell script. The shell scripts is actually a fun bit: why use an AI for something that can be deterministic? We wrote a bunch of scripts that CI runs to enforce various things like the aforementioned docs. We also force any changes to agent skills / agents.md go into their own PR. Final results, before vs after: PRs merged: 353 → 684 (80/wk → 154/wk, +94%) Merged within 1h: 28% → 45%; within 24h: 76% → 80% Human-reviewed PRs median merge time: 26h No human-review median merge time: 1h EJ Campbell Should be 60 PR's a day. Get cracking. Mike Julian @mikejulian · 6h On average, we’re basically there now. Average almost 700 PRs per week for a team of 6-7
[What work is automated or augmented; deskilling vs. upskilling; human-in-the-loop / on-the-loop arrangements.]
13.2.2 Oversight, accountability, and organizational trust
[Supervision, liability, calibrated reliance, organizational policy.]
13.2.3 Open questions and design implications
[Bullet or short prose: research gaps and practitioner takeaways.]
13.3 AI and Automation at Home
[Domestic and consumer settings: smart home, assistants, entertainment, health/wellness, caregiving, privacy in shared households.]
13.3.1 Everyday assistance and ambient automation
[Convenience, habit formation, invisible automation, failure modes.]
13.3.2 Privacy, intimacy, and household dynamics
[Data collection, multi-user households, vulnerable users, dark patterns.]
13.3.3 Open questions and design implications
[Research gaps and practitioner takeaways.]
13.4 AI and Automation at School
[Education contexts: tutoring systems, generative AI for writing/coding, assessment, classroom management, teacher tools, academic integrity.]
13.4.1 Learning, tutoring, and assessment
[Adaptive learning, feedback quality, over-reliance, skill acquisition.]
13.4.2 Teachers, institutions, and integrity
[Pedagogy change, policy, authorship, equity of access.]
13.4.3 Open questions and design implications
[Research gaps and practitioner takeaways.]
13.5 Cross-Cutting Themes
[Optional bridge section. Candidates: literacy and mental models; dependence and deskilling; equity and access; children vs. adults; transfer of trust across settings; measurement and evaluation.]
13.6 Conclusions
[Synthesize similarities and differences across the three settings; restate design/research priorities.]
Acknowledgments
[Funding, course origin, or other acknowledgments.]
Conflicts of Interest
The authors declare no conflict of interest.
AI Usage Statement
Generative AI models were used for additional research, to identify missing concepts, to support better organization, and for editorial tasks such as formatting, evaluating grammar/clarity and citation collation. Content and ideas are otherwise original to the human author contributors.
How to Cite This Chapter
[Author surnames, Initials] (2026). AI and automation at work, home, and school. In Shane T. Mueller (Ed.), A Handbook of Human-AI and Human-Automation Interaction. https://pages.mtu.edu/~shanem/human_ai/