A new field — and what it is already showing us.
Todd McCaffrey, MSc Cyberpsychology
Each capability is a gain — and each breaks an assumption HCI was built on. Hence a new field.
The label hasn’t settled yet. That is the first useful fact about the field.
Ergonomics, control panels, cockpits. Deterministic machines.
The PC. Usability, GUIs, mental models. Software obeys.
Systems that generate, adapt and act. Software collaborates.
How people and AI systems communicate and collaborate. CHI, IUI, HAXD conferences.
Keep human control high while automation is high. Reliable, safe, trustworthy beats autonomous.
HAI 2026, Osaka: “From Interaction to Agency: Navigating Autonomy.”
Knowing how to use, question and live with AI. A legal obligation for deployers since Feb 2025.
When should a person rely on the system — and when not? Over-reliance and under-reliance are both failures.
What do people believe the system is doing? Usually something simpler and more human than the truth.
How does a system say “I might be wrong” in a way people actually use?
Who decides, who acts, who is accountable — when the system can act on its own.
What happens to memory, reasoning and skill when thinking is shared with a machine?
Writing, coding, designing with an AI partner: who owns the idea, and does the output converge?
The Human-Centered AI framework: control and automation as two axes, not a trade-off.
The 18 guidelines and HAX Toolkit; and the leading group on what GenAI does to thinking.
Institutional centre of gravity: policy, indices, cross-disciplinary funding.
Mental models of AI tools, effects on team collaboration; mixed-method experiments to academic standards.
Where the agenda is set. HAI 2026 (Osaka, Nov): “From Interaction to Agency.”
Six observations from the first three years of everyone using AI.
productivity for the least experienced support agents (+14% overall, 5,172 agents)
time on professional writing, quality up 0.45 SD, and the skill gap narrowed (453 professionals)
quality for consultants on tasks inside the model’s reach — and worse outside it (758 at BCG)
Delegating mental work to an external aid. Calculators did it. Search did it. AI does it for synthesis, judgment and evaluation — the verbs, not the nouns.
participants in Gerlich (2025): heavier AI use ↔ lower critical-thinking scores, mediated by offloading
clinicians’ unassisted tumour detection three months after AI support was introduced
effort vs time: AI assistance always cuts effort, not time — the “speedup illusion”
Younger users offload more, and score lower. Higher education buffers it regardless of AI use.
Offloading a task you already know costs little. Offloading one you’re trying to learn means the skill doesn’t form. Shen & Tamkin (2026) show this for coding.
In large-scale RCTs, people with AI help gave up sooner on hard problems and did worse afterwards without it.
AI removes extraneous load (good) and germane load (the effort that is the learning). Both feel like the same relief.
Trust in the AI predicts less critical thinking; self-confidence predicts more (Lee et al., CHI 2025). Output feels finished before it’s checked.
Users build a theory of the AI’s mind; the AI builds one of theirs. Both are usually wrong, and the errors compound.
Emotionally responsive AI reduces independent judgment — measurably more in younger users.
Across many users, LLM-assisted writing and ideas homogenise. The collective-level cost of individual convenience.
Ask, read, decide yourself. The chatbot era, 2023.
Drafts, completions, copilots. You still press the button.
Books, buys, codes, emails. You set the goal; it acts.
Multi-step goals, own initiative. HAI 2026’s “shared autonomy”.
Volume of evidence narrows as rigour and time-horizon rise. “Nascent — further studies warranted.” — Int’l AI Safety Report 2026
Nobody. The gap every paper names.
Almost nothing beyond three-month windows.
A few in 2026 — persistence, independent performance, effort.
The bulk of the literature. Correlational, self-report.
Ask for counter-arguments, errors, alternatives. A system that only agrees is the one that offloads your judgment.
Offload what you already know. Do the parts you’re trying to learn yourself — then check them with the machine.
Know what it can’t do, how it fails, and when to distrust it. Since February 2025 that is a legal obligation for anyone deploying AI at work.