# The Five Topics the Owner of This Site Has Researched Most, and the Mainstream of Research on Each

- Author: Shuichiro Ogawa (Design Researcher / Design Consultant), https://jamjet-iqbal.com/
- Published: 2026-11-01; updated: 2026-11-01
- Canonical (HTML): https://jamjet-iqbal.com/en/notes/interest-top5-mainstream-trends/
- Japanese version: https://jamjet-iqbal.com/notes/interest-top5-mainstream-trends/ (Markdown: https://jamjet-iqbal.com/notes/interest-top5-mainstream-trends.md)
- Language: en
- Keywords: meta, interest, mainstream, research-trends, human-AI-collaboration, AI-agents, validity, psychometrics, gap-spotting, problematization, designerly-ways-of-knowing, literature-review

> We grouped the 233 Japanese notes on this site (as of 2026-11-01, including unpublished notes) into topics by rules on their slugs, excluding the regular watch notes and the notes about the site itself. In descending order, the topics were the designer's profession and practice (40 notes), generative AI tools and mechanisms (32), ability and assessment (26), research methods and novelty (26), and design theory and its history (23). For each topic, we identified the current mainstream of research in the field, using statements in systematic reviews, citation counts of representative works, and recent paper counts. For the profession, the mainstream is complementarity through human-AI collaboration (the review of 48 studies by Figoli and Rampino 2026 and of 93 papers by Shi et al. 2023), and papers with collaboration terms in the title or abstract outnumber those with replacement terms by about five to one. The counter-currents are evidence of declining demand (a 17% decrease in image-creation job posts in Demirci et al. 2025), evidence of homogenization (Doshi and Hauser 2024), and critiques of the word collaboration. For tools, the mainstream is agents built around an LLM as the controller with memory, planning, and tool use (Wang et al. 2024, whose yearly citations rose from 9 in 2023 to 1,045 in 2026); the counter-currents are mixed-initiative HCI that keeps the initiative with people, and critiques that show the gap to human performance (14.41% versus 78.24% on WebArena) and bias in evaluation. For ability and assessment, the mainstream is the psychometric framework that asks whether the interpretation and use of scores are valid (the 2014 Standards of AERA, APA, and NCME) and research on individual differences built on it (a reported heritability of 49% across twin studies); the counter-currents are critiques of cumulative advantage and meritocracy and views that place ability in situations. For methods and novelty, the mainstream is gap-spotting, in which research questions are built by finding gaps in the literature (the most common way among 52 articles in Sandberg and Alvesson 2011); the counter-currents are problematization, which challenges assumptions, and scientometrics that measures novelty through combinations of citations. For theory, the mainstream is the line of Cross, Schön, and Dorst that locates a knowledge distinctive to design in reflection-in-action and the co-evolution of problem and solution; a review of protocol studies (Hay et al. 2017) also treats this line and Simon's problem solving as the two paradigms. In yearly citations, however, representative works on the pluriverse and design justice have exceeded the mainstream works since 2024. Across all five topics, the owner's notes stand more often on the side that questions the premises and scope of the mainstream than on the side that writes within it.

## How were the topics counted?

If the notes on this site are ordered by what the owner has researched most, which topics come first?
We assigned each of the 233 Japanese notes on the site as of November 1, 2026 (including unpublished notes) to one topic, using rules on the words in their slugs (the names of the notes).
We then counted the notes per topic (the rules and table are in `source/review/interest-top5-mainstream-trends/papers.md`).
The 22 weekly and monthly watch notes and the 11 notes about the site itself were excluded because they do not reflect topics of interest.

| Rank | Topic | Notes | Published |
|---|---|---|---|
| 1 | The designer's profession and practice | 40 | 35 |
| 2 | Generative AI tools and mechanisms | 32 | 29 |
| 3 | Ability and assessment | 26 | 16 |
| 3 | Research methods and novelty | 26 | 22 |
| 5 | Design theory and its history | 23 | 13 |

The sixth topic, learning and education, has 22 notes, one fewer than the fifth.
The assignment relies on the words in the slugs, not on reading the content.
For example, a note on whether AI widens differences in people's thinking ability was assigned to ability, although it can also be read as being about tools or learning.

Next, for each topic, we selected the single most mainstream line of research in the corresponding field.
Here, "mainstream" means the majority of research in the field, not the majority among the owner's notes.
For each topic, one researcher agent proposed a candidate, and we checked it against three kinds of evidence: statements in systematic reviews, citation counts of representative works, and recent paper counts.
Numbers and claims were checked against the full text where we could obtain it and against the abstract otherwise, and we retrieved the paper and citation counts again from OpenAlex and Europe PMC (see Unverified items at the end).

## How does research describe the change in the designer's profession under AI?

The mainstream is complementarity through human-AI collaboration.
This line assumes that humans and AI have different strengths and that, combined, they can accomplish tasks neither can achieve alone; on this basis, it studies how roles are divided between designers and AI and what forms their interaction takes.
A systematic review of human-AI collaboration in design research narrowed 568 Scopus records to 48 studies and placed this assumption in its introduction (Figoli and Rampino 2026).
Another review of 93 papers asked how designers and AI can augment each other's complementary strengths and proposed centering research on collaborative creativity (Shi et al. 2023, abstract).
Representative works in this line include a study that coded 92 existing co-creative systems with a framework for describing co-creative interaction (Rezwana and Maher 2023) and a framework that separates levels of automation from levels of human control and aims for both to be high (Shneiderman 2020).

Paper counts also favor collaboration terms.
In OpenAlex, papers whose title or abstract contains designer and generative AI terms increased from 159 in 2023 to 510 in 2024 and 980 in 2025.
Of these, papers with collaboration terms (collaboration, co-creation, and others) numbered 34, 111, and 244, and papers with replacement terms (replacement, displacement, and others) numbered 7, 20, and 48, a difference of about five to one in every year[^t1count].

There are three counter-currents.
The first is evidence of declining demand: within eight months of the release of ChatGPT, job posts for automation-prone writing and coding work decreased by 21%, and after the release of image-generating AI, job posts for image creation decreased by 17% (Demirci et al. 2025, abstract).
Freelancers in highly affected occupations lost both work and earnings, and those with strong past records were not protected (Hui et al. 2024, abstract).
The second is evidence of homogenization: stories written with ideas from generative AI were rated higher individually but became more similar to each other (Doshi and Hauser 2024).
The third is a critique of the word collaboration itself; Sarkar (2023) argued that the phrase is misleading and takes away the credit for AI "labour" from the humans behind it.
Evidence on the effects of collaboration does not simply support the mainstream either.
A meta-analysis of 106 experimental studies found that, on average, human-AI combinations performed worse than the better of humans or AI alone (Hedges' g = −0.23), with gains in tasks that involve creating content and losses in tasks that involve making decisions (Vaccaro et al. 2024).

The owner's notes divide into those written along the mainstream and those that measure what the mainstream does not look at.
A synthesis of the shift in roles from execution toward direction ([Designer Careers That Will Generate Value over the Next Five Years — Academic Review (2026)](https://jamjet-iqbal.com/en/notes/designer-career-value-literature/)) follows the mainstream.
In contrast, notes that examine the chain by which commissioners grow used to low-quality AI output and the culture of making shrinks ([Do Decision-Makers Without Taste Stop Commissioning Creators? Checking the Problem of Image-Generating AI Through Three Links (Commissioning, Habituation, and the Creator Base) and Relating It to Research on AI Slop](https://jamjet-iqbal.com/en/notes/genai-image-taste-commissioning-literature/)), stagnating salaries ([Structural Factors behind Designer Pay Stagnation — Academic Review (2026)](https://jamjet-iqbal.com/en/notes/designer-salary-stagnation-literature/)), and the paradox of democratization and recommodification ([The Democratization-Recommodification Paradox: How AI Turns 'Anyone Can Do It' into 'No One Can Charge for It'](https://jamjet-iqbal.com/en/notes/democratization-recommodification-paradox/)) stand on the side of the counter-currents.

## What is the mainstream in generative AI tools and mechanisms?

The mainstream is agents based on LLMs.
In this line, an LLM serves as the controller, and memory, planning, and tool use are added so that the system acts over multiple steps in an environment.
The survey by Wang et al. (2024) described a surge in research on LLM-based autonomous agents and proposed a framework of four modules: profiling, memory, planning, and action.
The survey by Xi et al. (2025) likewise stated that many researchers have leveraged LLMs as the foundation for building AI agents and proposed a framework of three components: brain, perception, and action.
ReAct (Yao et al. 2023), which generates reasoning traces and actions in an interleaved manner, is an early representative work in this line.
A further survey calls systems with multi-agent collaboration, dynamic task decomposition, and persistent memory "agentic AI" and distinguishes them from single AI agents (Sapkota et al. 2026).
The yearly citations of Wang et al. (2024) rose from 9 in 2023 to 249 in 2024, 624 in 2025, and 1,045 in 2026 (through November 1).

The counter-currents are an HCI line that questions delegation from the human side and critiques of the narrative of capability.
The principles of mixed-initiative interfaces, which couple automated services with direct manipulation (Horvitz 1999), and a framework that keeps human control (Shneiderman 2020) ask, from the human side, how far tasks should be delegated to agents.
On the critical side, Kapoor et al. (2024) pointed out that agent evaluation focuses narrowly on accuracy, that state-of-the-art agents are needlessly complex and costly, and that agents overfit to benchmarks.
When agents and humans perform the same tasks, the best GPT-4-based agent achieved a success rate of 14.41% on web tasks, compared with 78.24% for humans (WebArena, Zhou et al. 2024); on computer tasks, humans accomplished 72.36% and the best model 12.24% (OSWorld, Xie et al. 2024).
Other work anticipates the harms of increasingly agentic systems (Chan et al. 2023).

Many of the owner's notes measure this mainstream from the outside.
A representative example traces how often the idea of AI agents has risen and fallen over 70 years and how design each time supplied a vocabulary for the scope of delegation and its presentation ([How Many Times Have AI Agents Been in Fashion over 70 Years, and What Did Design Do Each Time? (Academic Research)](https://jamjet-iqbal.com/en/notes/ai-agent-history-design-literature/)).
Some notes use the mainstream tools in practice ([Agentic Coding: The Current State of Orchestration Patterns (2026)](https://jamjet-iqbal.com/en/notes/agentic-coding-orchestration-patterns/)), but more of them question side effects, such as the mechanisms of sycophancy ([Why Do LLMs Become Sycophantic? How Preference Learning, Internal Circuits, and Input Framing Produce the Behavior](https://jamjet-iqbal.com/en/notes/llm-sycophancy-mechanisms-literature/)) and the finding that the effort of checking correctness remains even as more people can build software ([What Has Generative AI Changed About Making for Oneself? A Map of 59 Sources](https://jamjet-iqbal.com/en/notes/genai-personal-software-literature/)).

## What is the mainstream of research on ability and assessment?

The mainstream is the psychometric framework of validity and the research on individual differences built on it.
In this view, ability is a construct that cannot be observed directly, and how well test scores represent it must be established with evidence for each interpretation and use of the scores.
The standards for educational and psychological testing (AERA, APA, and NCME 2014) define validity as "the degree to which evidence and theory support the interpretations of test scores for proposed uses of tests" and treat fairness as a fundamental validity issue.
This definition rests on the classic work on construct validity (Cronbach and Meehl 1955) and on Messick (1995, abstract), who integrated validity, previously divided into content, criterion, and construct types, into a single construct validity that includes score meaning and social consequences.

Research on individual differences within this framework measures where differences in ability come from and what they predict.
In a meta-analysis of fifty years of twin studies, the reported heritability across 17,804 traits from 2,748 publications was 49% (Polderman et al. 2015, abstract).
When the overcorrection for range restriction was fixed, the validity estimates of personnel selection procedures decreased by .10 to .20, and structured interviews ranked first, although the procedures were judged to remain useful (Sackett et al. 2022, abstract).
These corrections were themselves made with the tools of the mainstream framework (corrections in meta-analysis).
In Europe PMC, papers containing "psychometric properties" increased from 2,171 in 2010 to 13,446 in 2025, and those containing "construct validity" from 1,709 to 10,857[^t3count].

There are two counter-currents.
The first is the critique of cumulative advantage and meritocracy, which holds that differences grow from early advantage as well as from ability.
Merton (1968, abstract) described the Matthew effect, in which contributions by eminent scientists become more visible and those by less known authors less visible, and DiPrete and Eirich (2006, abstract) reviewed cumulative advantage as a general mechanism of inequality.
Even with nearly identical review scores, applicants just above the funding threshold accumulated more than twice as much research funding over the following eight years as those just below it (Bol et al. 2018, abstract).
The second places ability in situations rather than fixing it inside the person.
Mischel and Shoda (1995, abstract) reconciled the invariance of personality with the variability of behavior across situations through if-then patterns specific to situations.
The mainstream definition of validity has also been challenged.
Borsboom et al. (2004, abstract) redefined validity as the existence of an attribute whose variations causally produce variation in measurement outcomes, contrasting this with current validity theory, which focuses on interpretation and correlation.
Within behavior genetics, Turkheimer (2000, abstract) wrote that twin studies do not show that genes are more fundamental than environments.

Some of the owner's notes use the mainstream tools, but their center of gravity lies on the side that questions interpretation and use.
The notes on effort and heritability ([Is the Ability to Make an Effort Also a Talent? The Heritability of Grit, Self-Control, and Motivation to Learn, and How Far These Traits Can Be Changed](https://jamjet-iqbal.com/en/notes/effort-talent-heritability-literature/)) and on LLMs as evaluators ([Recent Currents in LLM-as-a-Judge: A Literature Map of 53 Core Studies and a Standalone Chapter on Creativity Evaluation](https://jamjet-iqbal.com/en/notes/llm-as-a-judge-literature/)) were written with the mainstream tools, and the former states that heritability is not a measure of how unchangeable a trait is.
The notes on cumulative advantage ([Do Those Who Have Receive More? Mechanisms, Causal Identification, and Limits of Cumulative Advantage and the Matthew Effect in the Academic Literature](https://jamjet-iqbal.com/en/notes/cumulative-advantage-literature/)) and initial advantage ([Is the First Advantage Luck? Option Luck and Brute Luck, Ex Ante and Ex Post, and the Evidence on Initial Advantages and Fairness Judgments in the Academic Literature](https://jamjet-iqbal.com/en/notes/initial-advantage-luck-literature/)) stand on the side of the first counter-current, and a note on measuring dispositions as tendencies conditional on person and situation ([When Is a Disposition Being Measured? Measurement as Conditional Tendency, and Measurement as Co-occurrence](https://jamjet-iqbal.com/en/notes/disposition-measurement-literature/)) stands on the side of the second.

## How are research questions and novelty mainly established?

The mainstream is gap-spotting: building research questions by finding gaps in prior research and claiming novelty for filling them.
Sandberg and Alvesson (2011, abstract) examined 52 articles in organization studies and reported that the most common way of constructing research questions across paradigmatic camps was to spot gaps in the literature, describing the dominance of gap-spotting as surprising.
Among 124 articles in five top-ranked higher education journals, most articles also had a gap statement (Wald et al. 2024, abstract).
The same study, however, found that 20% of the articles had no gap statement and 27% gave no justification for the importance of the research.
Critics of the mainstream also acknowledge that established ways of arriving at research questions mean spotting or constructing gaps (Alvesson and Sandberg 2011, abstract).

There are two counter-currents.
The first is problematization, which builds research questions by identifying and challenging the assumptions underlying existing literature (Alvesson and Sandberg 2011, abstract).
Its source lies in the argument of Davis (1971, abstract) that interesting theories deny certain assumptions of their audience.
The second is scientometrics, which measures novelty through combinations of cited works; analyzing 17.9 million papers, Uzzi et al. (2013, abstract) reported that papers grounded in highly conventional combinations with an intrusion of unusual combinations were twice as likely to be highly cited.
Across 45 million papers and 3.9 million patents, work that breaks with the past and pushes science and technology in new directions has become less common over time (Park et al. 2023, abstract).

The owner's notes most often stand on the side that criticizes the mainstream.
Examples are an essay that searches unexplored areas of AI research through framing leaps that invert self-evident assumptions ([Where Are the Gaps in AI Research? Seven Voids Found by Reframing](https://jamjet-iqbal.com/en/notes/ai-research-gaps-abduction/)) and a note on methods for problem definition ([An Academic Map of Methods for Reframing Problems: From Abduction-2 to Problem Structuring](https://jamjet-iqbal.com/en/notes/problem-definition-methods-literature/)).
A note that examined where novelty resides in a paper ([Where Do Novel Research Questions Come From? The Four Loci and Their Combinations, Examined Against the Literature](https://jamjet-iqbal.com/en/notes/novel-research-questions-literature/)) repositions the mainstream form (the section that claims a gap) as one of several sites of novelty.

## What is the mainstream in design theory?

The mainstream is the line holding that design has its own type of knowledge and that this knowledge appears in thinking while coping with indeterminate situations (reflection-in-action and the co-evolution of problem and solution).
Representative works are Cross (1982) on designerly ways of knowing, Schön (1983) on reflective practice, Dorst and Cross (2001) on the co-evolution of problem and solution, and Dorst (2011).
Schön placed reflection-in-action at the center of the capacity of professionals to cope with the unique, uncertain, and conflicted situations of practice.
Cross (2001) started from Simon's call for a science of design in universities, traced the lineage of attempts to make design a science, and argued for design as a discipline.
A systematic review of 47 protocol studies (Hay et al. 2017) identified two fundamental paradigms in design cognition research: the problem-solving paradigm founded in the theories of Newell and Simon, and the reflective paradigm heavily influenced by Schön's work on reflection-in-action.
Of the 24 cognitive processes that the same review drew from 33 studies, 10 (41.7%) belonged to design as search and 14 (58.3%) to design as exploration, which includes co-evolution and unexpected discovery.
Digby (2026), who attacks the mainstream, also lists these accounts as the foundations of "the prevailing epistemologies of design."

The momentum, however, is shifting.
The eight representative works of the mainstream have 10,927 citations in total, more than the 6,006 of seven representative works on the pluriverse, transition design, design justice, and more-than-human design.
In yearly citations, the mainstream led the counter-current by 909 to 738 in 2023, but the order reversed in 2024 at 799 to 872, and the gap widened to 924 to 1,189 in 2025[^t5count].
The mainstream leads in cumulative citations and in reviews, and the counter-current leads in yearly citations.

There are two counter-currents.
The first questions the premise of a universal design knowledge and the human-centered view: Escobar (2018, abstract) argued for an autonomous design that departs from commercial and modernizing aims, and Costanza-Chock (2020, abstract) for design led by marginalized communities that challenges structural inequality.
The second is an epistemological critique: Kimbell (2011, abstract) pointed out that many accounts of design thinking rely on a dualism between thinking and doing, and Digby (2026) argued that mainstream accounts remain primarily descriptive and that design does not require a distinct epistemology.

The owner's notes engage with this line from the side of philosophy and history.
A map of the philosophy of design ([What Is the Philosophy of Design? (Academic Research)](https://jamjet-iqbal.com/en/notes/philosophy-of-design-literature/)) and a rereading of the claim that design as a discipline is absent ([Was the 'Absence of a Science of Design' Asserted Three Times? Rereading the Observations of 1978, 2008, and 2023 in Their Sources](https://jamjet-iqbal.com/en/notes/design-discipline-absence-literature/)) place the mainstream at their core.
A note that read Digby's critique in full ([Does Design Need Its Own Epistemology, and Can Philosophy Be Done by Design? (Reading Two 2026 Texts)](https://jamjet-iqbal.com/en/notes/designerly-epistemology-debate-literature/)) examined the argument of the critique itself.
The empirical pillars of the mainstream (Cross 1982, Dorst and Cross 2001, Dorst 2011) have been read only at the level of bibliographic records or abstracts in the owner's ledgers as well.

## The five mainstreams and where the owner's notes stand

Each of the five mainstreams is a framework on which much research in its field relies.
Complementarity through collaboration, LLM-based agents, psychometric validity, gap-spotting, and the line of design-specific knowledge are each described in reviews, standards, or analyses of the literature as the most common line or as a foundational framework.
Citation counts, however, show different relations between mainstream and counter-current across topics.
In methods, the critical side, Alvesson and Sandberg (2011), has 1,584 citations, more than the 787 of Sandberg and Alvesson (2011), which documented the mainstream; in theory, the counter-current leads in yearly citations.

In all five topics, the owner's notes stand more often on the side that questions the premises and scope of the mainstream than on the side that writes within it.
Many notes address declining demand and homogenization in the profession, side effects and delegation in tools, interpretation, use, and early advantage in ability, the questioning of assumptions in methods, and the examination of critiques of the mainstream epistemology in theory.
There are also notes written with the mainstream tools themselves (on effort and heritability, on agents in practice, and on LLM evaluators), and the owner uses those tools while checking how far the mainstream can go.

## What this count can and cannot say

This count can say that, as of November 1, 2026, the owner's notes have dealt most with five topics (the designer's profession, generative AI tools, ability and assessment, research methods, and design theory) and that the mainstream of each field is the line described above.

There is also much it cannot say.
The assignment relies on the words in the slugs and does not count notes that span several topics.
The number of notes is only one indicator of interest; the length of the notes and the number of revisions were not included in the ranking.
"Mainstream" refers to the line on which reviews, citations, and paper counts agree; it does not rule out other candidates (for example, labor-economic research on demand in the profession).
Paper counts come from term searches that ignore context (a sentence denying collaboration is also counted as collaboration).
The crossing of citations in theory may change with the choice of representative works, but the counter-current also overtook the mainstream in 2024 when the researcher agent counted a different set of eight works.

## Related notes

- [Designer Careers That Will Generate Value over the Next Five Years — Academic Review (2026)](https://jamjet-iqbal.com/en/notes/designer-career-value-literature/): academic literature on how designers' careers change in the AI era
- [How Many Times Have AI Agents Been in Fashion over 70 Years, and What Did Design Do Each Time? (Academic Research)](https://jamjet-iqbal.com/en/notes/ai-agent-history-design-literature/): seventy years of AI agent booms and the role of design
- [Is the Ability to Make an Effort Also a Talent? The Heritability of Grit, Self-Control, and Motivation to Learn, and How Far These Traits Can Be Changed](https://jamjet-iqbal.com/en/notes/effort-talent-heritability-literature/): behavior genetics on whether effort is also a talent
- [Where Do Novel Research Questions Come From? The Four Loci and Their Combinations, Examined Against the Literature](https://jamjet-iqbal.com/en/notes/novel-research-questions-literature/): literature on where novelty resides in a paper
- [What Is the Philosophy of Design? (Academic Research)](https://jamjet-iqbal.com/en/notes/philosophy-of-design-literature/): a map of the philosophy of design
- [Research Trends in HCI since Generative AI Became Usable (2023–2026): Many Systems Built, and the Checking Work Left to People](https://jamjet-iqbal.com/en/notes/ai-hci-research-trends/): research trends in HCI since generative AI became usable

## Unverified items

No major claim relies on unverified material.
For the following works, we could not obtain the full text; we wrote about them only within the scope of the abstract and marked this in the text.
Alvesson and Sandberg (2011), Bol et al. (2018), Borsboom et al. (2004), Costanza-Chock (2020), Davis (1971), Demirci et al. (2025), DiPrete and Eirich (2006), Escobar (2018), Hui et al. (2024), Kimbell (2011), Merton (1968), Messick (1995), Mischel and Shoda (1995), Park et al. (2023), Polderman et al. (2015), Sackett et al. (2022), Sandberg and Alvesson (2011), Shi et al. (2023), Turkheimer (2000), Uzzi et al. (2013), and Wald et al. (2024).
For Cronbach and Meehl (1955), Cross (1982), Dorst and Cross (2001), and Dorst (2011), not even the abstract was available, and we used only bibliographic records and citation counts.
The numbers for WebArena and OSWorld are from the arXiv versions and were not checked against the peer-reviewed versions.
Kapoor et al. (2024) is a preprint, and we did not confirm a peer-reviewed version.

## References

### Topic 1: The designer's profession and AI

- Demirci, Ozge, Jonas Hannane, and Xinrong Zhu. 2025. "Who Is AI Replacing? The Impact of Generative AI on Online Freelancing Platforms." *Management Science* 71 (10): 8097–8108. https://doi.org/10.1287/mnsc.2024.05420
- Doshi, Anil R., and Oliver P. Hauser. 2024. "Generative AI Enhances Individual Creativity but Reduces the Collective Diversity of Novel Content." *Science Advances* 10 (28): eadn5290. https://doi.org/10.1126/sciadv.adn5290
- Figoli, Fabio Antonio, and Lucia Rampino. 2026. "Mapping the Human-AI Collaboration Trends in Design Research: A Systematic Review." *Interaction Design and Architecture(s)* 69: 258–285. https://doi.org/10.55612/s-5002-069-012
- Hui, Xiang, Oren Reshef, and Luofeng Zhou. 2024. "The Short-Term Effects of Generative Artificial Intelligence on Employment: Evidence from an Online Labor Market." *Organization Science* 35 (6): 1977–1989. https://doi.org/10.1287/orsc.2023.18441
- Rezwana, Jeba, and Mary Lou Maher. 2023. "Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systems." *ACM Transactions on Computer-Human Interaction* 30 (5): 1–28. https://doi.org/10.1145/3519026
- Sarkar, Advait. 2023. "Enough With 'Human-AI Collaboration'." In *Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems*, 1–8. https://doi.org/10.1145/3544549.3582735
- Shi, Yang, Tian Gao, Xiaohan Jiao, and Nan Cao. 2023. "Understanding Design Collaboration Between Designers and Artificial Intelligence: A Systematic Literature Review." *Proceedings of the ACM on Human-Computer Interaction* 7 (CSCW2): 1–35. https://doi.org/10.1145/3610217
- Shneiderman, Ben. 2020. "Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy." *International Journal of Human–Computer Interaction* 36 (6): 495–504. https://doi.org/10.1080/10447318.2020.1741118
- Vaccaro, Michelle, Abdullah Almaatouq, and Thomas Malone. 2024. "When Combinations of Humans and AI Are Useful: A Systematic Review and Meta-Analysis." *Nature Human Behaviour* 8 (12): 2293–2303. https://doi.org/10.1038/s41562-024-02024-1

### Topic 2: Generative AI tools and mechanisms

- Chan, Alan, Rebecca Salganik, Alva Markelius, Chris Pang, Nitarshan Rajkumar, Dmitrii Krasheninnikov, Lauro Langosco, et al. 2023. "Harms from Increasingly Agentic Algorithmic Systems." In *Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency*, 651–666. https://doi.org/10.1145/3593013.3594033
- Horvitz, Eric. 1999. "Principles of Mixed-Initiative User Interfaces." In *Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '99)*, 159–166. https://doi.org/10.1145/302979.303030
- Kapoor, Sayash, Benedikt Stroebl, Zachary S. Siegel, Nitya Nadgir, and Arvind Narayanan. 2024. "AI Agents That Matter." arXiv:2407.01502. https://arxiv.org/abs/2407.01502
- Sapkota, Ranjan, Konstantinos I. Roumeliotis, and Manoj Karkee. 2026. "AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges." *Information Fusion* 126: 103599. https://doi.org/10.1016/j.inffus.2025.103599
- Wang, Lei, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, et al. 2024. "A Survey on Large Language Model Based Autonomous Agents." *Frontiers of Computer Science* 18 (6): 186345. https://doi.org/10.1007/s11704-024-40231-1
- Xi, Zhiheng, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, et al. 2025. "The Rise and Potential of Large Language Model Based Agents: A Survey." *Science China Information Sciences* 68 (2): 121101. https://doi.org/10.1007/s11432-024-4222-0
- Xie, Tianbao, Danyang Zhang, Jixuan Chen, Xiaochuan Li, Siheng Zhao, Ruisheng Cao, Toh Jing Hua, et al. 2024. "OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments." arXiv:2404.07972. https://arxiv.org/abs/2404.07972
- Yao, Shunyu, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2023. "ReAct: Synergizing Reasoning and Acting in Language Models." In *ICLR 2023*. arXiv:2210.03629. https://arxiv.org/abs/2210.03629
- Zhou, Shuyan, Frank F. Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, et al. 2024. "WebArena: A Realistic Web Environment for Building Autonomous Agents." In *ICLR 2024*. arXiv:2307.13854. https://arxiv.org/abs/2307.13854

### Topic 3: Ability and assessment

- American Educational Research Association, American Psychological Association, and National Council on Measurement in Education. 2014. *Standards for Educational and Psychological Testing*. Washington, DC: AERA. https://www.testingstandards.net/uploads/7/6/6/4/76643089/standards_2014edition.pdf
- Bol, Thijs, Mathijs de Vaan, and Arnout van de Rijt. 2018. "The Matthew Effect in Science Funding." *Proceedings of the National Academy of Sciences* 115 (19): 4887–4890. https://doi.org/10.1073/pnas.1719557115
- Borsboom, Denny, Gideon J. Mellenbergh, and Jaap van Heerden. 2004. "The Concept of Validity." *Psychological Review* 111 (4): 1061–1071. https://doi.org/10.1037/0033-295x.111.4.1061
- Cronbach, Lee J., and Paul E. Meehl. 1955. "Construct Validity in Psychological Tests." *Psychological Bulletin* 52 (4): 281–302. https://doi.org/10.1037/h0040957
- DiPrete, Thomas A., and Gregory M. Eirich. 2006. "Cumulative Advantage as a Mechanism for Inequality: A Review of Theoretical and Empirical Developments." *Annual Review of Sociology* 32: 271–297. https://doi.org/10.1146/annurev.soc.32.061604.123127
- Merton, Robert K. 1968. "The Matthew Effect in Science." *Science* 159 (3810): 56–63. https://doi.org/10.1126/science.159.3810.56
- Messick, Samuel. 1995. "Validity of Psychological Assessment: Validation of Inferences from Persons' Responses and Performances as Scientific Inquiry into Score Meaning." *American Psychologist* 50 (9): 741–749. https://doi.org/10.1037/0003-066x.50.9.741
- Mischel, Walter, and Yuichi Shoda. 1995. "A Cognitive-Affective System Theory of Personality: Reconceptualizing Situations, Dispositions, Dynamics, and Invariance in Personality Structure." *Psychological Review* 102 (2): 246–268. https://doi.org/10.1037/0033-295X.102.2.246
- Polderman, Tinca J. C., Beben Benyamin, Christiaan A. de Leeuw, Patrick F. Sullivan, Arjen van Bochoven, Peter M. Visscher, and Danielle Posthuma. 2015. "Meta-Analysis of the Heritability of Human Traits Based on Fifty Years of Twin Studies." *Nature Genetics* 47 (7): 702–709. https://doi.org/10.1038/ng.3285
- Sackett, Paul R., Charlene Zhang, Christopher M. Berry, and Filip Lievens. 2022. "Revisiting Meta-Analytic Estimates of Validity in Personnel Selection: Addressing Systematic Overcorrection for Restriction of Range." *Journal of Applied Psychology* 107 (11): 2040–2068. https://doi.org/10.1037/apl0000994
- Turkheimer, Eric. 2000. "Three Laws of Behavior Genetics and What They Mean." *Current Directions in Psychological Science* 9 (5): 160–164. https://doi.org/10.1111/1467-8721.00084

### Topic 4: Research methods and novelty

- Alvesson, Mats, and Jörgen Sandberg. 2011. "Generating Research Questions Through Problematization." *Academy of Management Review* 36 (2): 247–271. https://doi.org/10.5465/amr.2009.0188
- Davis, Murray S. 1971. "That's Interesting!" *Philosophy of the Social Sciences* 1 (2): 309–344. https://doi.org/10.1177/004839317100100211
- Park, Michael, Erin Leahey, and Russell J. Funk. 2023. "Papers and Patents Are Becoming Less Disruptive over Time." *Nature* 613 (7942): 138–144. https://doi.org/10.1038/s41586-022-05543-x
- Sandberg, Jörgen, and Mats Alvesson. 2011. "Ways of Constructing Research Questions: Gap-Spotting or Problematization?" *Organization* 18 (1): 23–44. https://doi.org/10.1177/1350508410372151
- Uzzi, Brian, Satyam Mukherjee, Michael Stringer, and Ben Jones. 2013. "Atypical Combinations and Scientific Impact." *Science* 342 (6157): 468–472. https://doi.org/10.1126/science.1240474
- Wald, Navé, Tony Harland, and Chandima Daskon. 2024. "The Gap Statement and Justification in Higher Education Research: An Analysis of Published Articles." *European Journal of Higher Education* 14 (2): 308–323. https://doi.org/10.1080/21568235.2023.2189132

### Topic 5: Design theory and its history

- Costanza-Chock, Sasha. 2020. *Design Justice: Community-Led Practices to Build the Worlds We Need*. Cambridge, MA: MIT Press. https://doi.org/10.7551/mitpress/12255.001.0001
- Cross, Nigel. 1982. "Designerly Ways of Knowing." *Design Studies* 3 (4): 221–227. https://doi.org/10.1016/0142-694X%2882%2990040-0
- Cross, Nigel. 2001. "Designerly Ways of Knowing: Design Discipline Versus Design Science." *Design Issues* 17 (3): 49–55. https://doi.org/10.1162/074793601750357196
- Digby, JJ. 2026. "Does Knowledge in Design Require a Distinct Epistemology? A Critical Rationalist Perspective." In *Proceedings of DRS 2026*. https://doi.org/10.21606/drs.2026.1608
- Dorst, Kees. 2011. "The Core of 'Design Thinking' and Its Application." *Design Studies* 32 (6): 521–532. https://doi.org/10.1016/j.destud.2011.07.006
- Dorst, Kees, and Nigel Cross. 2001. "Creativity in the Design Process: Co-Evolution of Problem–Solution." *Design Studies* 22 (5): 425–437. https://doi.org/10.1016/S0142-694X%2801%2900009-6
- Escobar, Arturo. 2018. *Designs for the Pluriverse: Radical Interdependence, Autonomy, and the Making of Worlds*. Durham, NC: Duke University Press. https://doi.org/10.1215/9780822371816
- Hay, Laura, Alex H. B. Duffy, Chris McTeague, Laura M. Pidgeon, Tijana Vuletic, and Madeleine Grealy. 2017. "A Systematic Review of Protocol Studies on Conceptual Design Cognition: Design as Search and Exploration." *Design Science* 3: e10. https://doi.org/10.1017/dsj.2017.11
- Kimbell, Lucy. 2011. "Rethinking Design Thinking: Part I." *Design and Culture* 3 (3): 285–306. https://doi.org/10.2752/175470811X13071166525216
- Schön, Donald A. 1983. *The Reflective Practitioner: How Professionals Think in Action*. New York: Basic Books (Routledge reissue, 2017). https://doi.org/10.4324/9781315237473

[^t1count]: OpenAlex title and abstract search (2026-11-01). The common query was (designer OR designers OR "design practice") AND ("generative AI" OR "large language model" OR LLM OR "text-to-image"). Collaboration terms were collaboration, co-creation, co-creative, augmentation, and augment; replacement terms were replacement, replace, displacement, and "job loss." Term searches ignore context, so the ratio is only a rough guide.

[^t3count]: Europe PMC phrase search of full texts and abstracts (2026-11-01), which covers mainly biomedical literature. "Measurement invariance" rose from 115 to 2,524 papers, "heritability" from 2,540 to 8,090, "meritocracy" from 17 to 236, and "cumulative advantage" from 18 to 119.

[^t5count]: Yearly citations in OpenAlex (2026-11-01; 2026 runs through November 1). The eight mainstream works were Cross 1982, Schön and Wiggins 1992, Dorst and Cross 2001, Cross 2001, Cross 2006 (book), Dorst 2011, Dorst 2015 (book), and Schön 1983 (record of the 2017 reissue); the seven counter-current works were Willis 2006, Irwin 2015, Forlano 2017, Escobar 2018, Costanza-Chock 2020, Giaccardi and Redström 2020, and Wakkary 2021. The list and DOIs are in the ledger. In 2026, the mainstream had 1,028 citations and the counter-current 1,434.