Forty years of human-computer interaction and knowledge media design: twelve challenges to meet in fewer than the next forty years
Bibliographic record
Abstract
Inspired in part by a seminal article by JCR Licklider on man-computer symbiosis [3, see also 4, 5], a wonderful course entitled Technological aids to human thought taught by Anthony Oettinger that I took at Harvard early in 1966, and the vitality and excitement of MIT Project Mac, the AI Lab, and especially Lincoln Lab [2], I began research in interactive computing shortly after the September 1965 start of my Ph.D. work at M.I.T. Now, 40 years later, receiving this honour (with gratitude) allows me the indulgence to rant for at least 40 minutes, reflecting first on the miracles in processor speed, memory capacity, bandwidth, I/O technology, graphics algorithms, and human-computer interfaces that have transpired over this interval [see also 1], and then speaking at much greater length over things that remain undone.The latter topics will be organized into two categories, compelling research challenges (junior faculty without tenure and Ph.D. students searching for topics listen carefully ***), and broader challenges for the fields of human-computer interaction and knowledge media design (senior faculty with tenure seeking to slay dragons listen even more carefully ***).I will briefly sketch and articulate the following six research challenges:• Collaboration technologies --- why are these tools still so hard to use?• Intelligent interfaces --- can AI finally aid humans instead of aiming to replace them, or, why can the computer beat Kasporov, but cannot connect me to the Net?• Design methodologies --- can we do less boasting about being user-centred and start doing better science?• Evaluation methodologies --- how can we gather design intelligence by mining rich potential sources of user experience data from the field?• Interfaces for seniors --- what we can do for seniors and what can they can do for us?• Electronic memory aids --- is this a compelling area promising a major payoff for human productivity and morale?I will then rant for as long as possible on the following six broader issues:• Courses on computers and society and communication skills for computer science students --- if we don't insist that this be taught, and take the lead, who will?• Interfaces in context --- why do I teach knowledge media design and not user interface design?• HCI in computer science departments --- should we continue to pretend that we do computer science?• Open source and open access --- if the intellectual property and technology transfer system is broken, shouldn't we try to fix it?• Appropriate automation --- can it and will it ever stop?• Interfaces everywhere --- is change possible, and how can we make things better?
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.019 | 0.037 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".