Cited papers
The 50 papers I have cited most often in my notes, ranked by how many notes cite them. I pick the topic of each note from whatever I am curious about at the time. Even so, some papers keep coming back in unrelated notes, and their ranking shows where I read the academic literature from.
The counts are recomputed by the site build every time a note is written.
- Notes counted
- 169
- Papers cited
- 2,939
- Cited by two or more notes
- 354
- Listed on this page
- 50
Areas
The 50 listed papers are grouped into seven areas by how the notes use them, and their citations (222 in total) are summed by area.
- Design theory and methods 60 citations (27%), 13 papers
- Generative AI and creativity 37 citations (17%), 6 papers
- Science and AI models 28 citations (13%), 6 papers
- Learning, cognition and ability 27 citations (12%), 6 papers
- AI, work and the economy 25 citations (11%), 8 papers
- Research questions and methods 23 citations (10%), 4 papers
- AI in design practice and education 22 citations (10%), 7 papers
Ranking
Papers cited by the same number of notes share a rank and are ordered by the date I first cited them. 43 papers are cited by 3 notes; to keep the list at 50, only the 19 I cited earliest are listed.
Cited by 11 notes (1)
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Rank 1
Generative AI and creativity
Generative AI enhances individual creativity but reduces the collective diversity of novel content ↗
Anil R. Doshi & Oliver P. Hauser (2024). Science Advances
An online experiment in which writers received story ideas from an LLM shows that AI ideas made stories rated more creative, especially for less creative writers, but made the stories more similar to one another.
Cited 887 times worldwide · First cited here 2026-06-28
Cited in 11 notes
- The Economics of AI and Design — Reading the Structural Transformation of Design Labor Through Six Economic Theories
- Competency Measurement Premised on AI Use — The Aptitude x AI Skill Interaction and Measurement Frameworks
- How Novelty Is Made and How It Is Claimed: A Typology of Establishing Strategies
- Which Angle for a Generative-Art Review Paper Attracts Citations? An Empirical Analysis Using Bibliographic Data
- AI Slop: Reading It as Outsourced Verification, Not Low Quality
- Does Seeming Common Online Mean It Is Common?
- When Judgment Becomes a Function Call, What Moves in Design
- What Social Coordination Costs Arise from Collaborating with Others on Creative Work
- Asking an LLM for Design: Briefs, Skills, and a Process That Keep UIs from Converging on the Obvious
- What Does It Take to Produce Novelty Now? Separating Traits, Attitudes, Stances, Abilities, and Skills
- Does Prototyping Widen Design Exploration? Fixation on Early Examples, Parallel Prototyping, Rapid Prototyping with Generative AI, and Documentation in Research through Design
Cited by 9 notes (1)
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Rank 2
Design theory and methods
The core of ‘design thinking’ and its application ↗
Kees Dorst (2011). Design Studies
Distinguishes abduction-1, finding the 'what', from abduction-2, creating the frame and the 'what' together, and places frame creation at the core of design thinking.
Cited 1,880 times worldwide · First cited here 2026-07-09
Cited in 9 notes
- Activating Reframing in Design: Cognitive Mechanisms and Practical Methods
- Is the Primacy of Qualitative Methods in Design Research Academically Mainstream?
- Where Are the Gaps in AI Research? Seven Voids Found by Reframing
- AI Research Gaps, a Third Time: Rereading Them as Rewritings of the Game Board
- The Epistemological Premises and Methodological Foundations of Design: A Literature Map of Wicked Problems, Abduction, and Set-Based Exploration
- Thinking in Problem Definition: Rereading the Game Board of Existing Research
- Gaps in AI Research, a Fourth Time: Seen from Outside the Inversion Family
- An Academic Map of Methods for Reframing Problems: From Abduction-2 to Problem Structuring
- Where Do Novel Research Questions Come From? The Four Loci and Their Combinations, Examined Against the Literature
Cited by 7 notes (4)
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Rank 3
Generative AI and creativity
The Effects of Generative AI on Design Fixation and Divergent Thinking ↗
Samangi Wadinambiarachchi, Ryan M. Kelly, Saumya Pareek et al. (2024). Proceedings of the CHI Conference on Human Factors in Computing Systems
A between-participants experiment (N=60) found that using an AI image generator during visual ideation increased fixation on an initial example and reduced the number, variety and originality of ideas.
Cited 193 times worldwide · First cited here 2026-06-07
Cited in 7 notes
- AI in the Design Industry: An Academic Review (2026)
- Designer Careers That Will Generate Value over the Next Five Years — Academic Review (2026)
- An Academic Map of Research That Treats Hallucination as a Resource for Creativity
- AI Slop: Reading It as Outsourced Verification, Not Low Quality
- Asking an LLM for Design: Briefs, Skills, and a Process That Keep UIs from Converging on the Obvious
- What Does It Take to Produce Novelty Now? Separating Traits, Attitudes, Stances, Abilities, and Skills
- Does Prototyping Widen Design Exploration? Fixation on Early Examples, Parallel Prototyping, Rapid Prototyping with Generative AI, and Documentation in Research through Design
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Rank 3
Research questions and methods
Research through design as a method for interaction design research in HCI ↗
John Zimmerman, Jodi Forlizzi & Shelley Evenson (2007). Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proposes research through design as a model for interaction design research in HCI and offers four lenses for evaluating its contributions (process, invention, relevance, extensibility), with three examples.
Cited 2,300 times worldwide · First cited here 2026-07-15
Cited in 7 notes
- Is the Primacy of Qualitative Methods in Design Research Academically Mainstream?
- Qualitative and Quantitative Research: In the Context of Design
- Methods and Validity in Design Research: A Literature Map of Case Study, Research through Design, and Mixed Methods
- Where Do Novel Research Questions Come From? The Four Loci and Their Combinations, Examined Against the Literature
- How Novelty Is Made and How It Is Claimed: A Typology of Establishing Strategies
- What Was Measured as "Gyaru-Mind": The Provenance of Eight Factors and Its Limits
- Does Prototyping Widen Design Exploration? Fixation on Early Examples, Parallel Prototyping, Rapid Prototyping with Generative AI, and Documentation in Research through Design
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Rank 3
Research questions and methods
Ways of constructing research questions: gap-spotting or problematization? ↗
Jörgen Sandberg & Mats Alvesson (2010). Organization
Reviewing 52 organization studies articles, shows that research questions are mostly built by gap-spotting, develops a typology of it, and proposes assumption-challenging ways that yield more interesting theories.
Cited 783 times worldwide · First cited here 2026-07-19
Cited in 7 notes
- AI Research Gaps, a Third Time: Rereading Them as Rewritings of the Game Board
- Analyzing the Making Process and Its Contextual Supports: A Literature Map of the Critical Incident Technique, Reflective Writing, and Design Education History
- Thinking in Problem Definition: Rereading the Game Board of Existing Research
- Gaps in AI Research, a Fourth Time: Seen from Outside the Inversion Family
- Where Do Novel Research Questions Come From? The Four Loci and Their Combinations, Examined Against the Literature
- How Novelty Is Made and How It Is Claimed: A Typology of Establishing Strategies
- Writing Material for a Review Article: The Assumption Ledger and Nearest-Neighbour Literature Left by 17 Novelty Audits
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Rank 3
Generative AI and creativity
Homogenization Effects of Large Language Models on Human Creative Ideation ↗
Barrett R Anderson, Jash Hemant Shah & Max Kreminski (2024). Creativity and Cognition
A 36-participant comparative study found that ChatGPT users produced more, and more detailed, ideas, but ideas that were less semantically distinct across users, and felt less responsible for them.
Cited 171 times worldwide · First cited here 2026-07-27
Cited in 7 notes
- How Novelty Is Made and How It Is Claimed: A Typology of Establishing Strategies
- Writing Material for a Review Article: The Assumption Ledger and Nearest-Neighbour Literature Left by 17 Novelty Audits
- AI Slop: Reading It as Outsourced Verification, Not Low Quality
- Does Seeming Common Online Mean It Is Common?
- Taking the LLM Smell Out of LLM Prose, and How to Bring humanizer Into This Project
- Asking an LLM for Design: Briefs, Skills, and a Process That Keep UIs from Converging on the Obvious
- Does Prototyping Widen Design Exploration? Fixation on Early Examples, Parallel Prototyping, Rapid Prototyping with Generative AI, and Documentation in Research through Design
Cited by 6 notes (4)
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Rank 7
Learning, cognition and ability
Hao-Ping (Hank) Lee, Advait Sarkar, Lev Tankelevitch et al. (2025). Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
A survey of 319 knowledge workers (936 examples) found that higher confidence in GenAI went with less critical thinking and higher self-confidence with more, and that critical thinking shifted toward verification and integration.
Cited 499 times worldwide · First cited here 2026-06-08
Cited in 6 notes
- Does AI Make Smart People Smarter and Everyone Else Shallower? — A Two-Lineage Debate Between Industry and Academia
- Research Currents in AI, Education, and the Learning Sciences: Evidence on Generative AI and Learning (2024–2026)
- AI, Psychology, and Cognitive Science Research Trends: Machine Psychology and the Cognitive Modeling of LLMs (2024–2026)
- Where Are the Gaps in AI Research? Seven Voids Found by Reframing
- Does Early Use of Generative AI Inhibit the Formation of Thought? A Literature Map of Cognitive Offloading and Learning
- What Does It Take to Produce Novelty Now? Separating Traits, Attitudes, Stances, Abilities, and Skills
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Rank 7
Design theory and methods
Dilemmas in a general theory of planning ↗
Horst W. J. Rittel & Melvin M. Webber (1973). Policy Sciences
Characterizes the problems of social planning as wicked problems with ten properties, arguing that formulating the problem is itself the problem.
Cited 15,665 times worldwide · First cited here 2026-07-09
Cited in 6 notes
- The Nexus of Design and Social Science: A Genealogy of Methods, Theories, and Institutions
- The Epistemological Premises and Methodological Foundations of Design: A Literature Map of Wicked Problems, Abduction, and Set-Based Exploration
- Thinking in Problem Definition: Rereading the Game Board of Existing Research
- A Genealogy of Practitioner Methods for Reframing Problems: Who Made Them, Traced to Their Origins
- An Academic Map of Methods for Reframing Problems: From Abduction-2 to Problem Structuring
- Adversarial Review of Two Generative Art Surveys: Examining the Literature Map and the Citation Strategy Against the Design-Theory Canon
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Rank 7
Design theory and methods
David G. Jansson & Steven M. Smith (1991). Design Studies
Shows experimentally that designers shown an example carry its features into their own designs even when told of its flaws, naming the phenomenon design fixation.
Cited 1,035 times worldwide · First cited here 2026-07-09
Cited in 6 notes
- Activating Reframing in Design: Cognitive Mechanisms and Practical Methods
- The Creativity of Subtraction: The Additive Bias, and the Hypothesis of Designing Absence
- Is Expertise a One-Way Street? Unlearning and the Blind Spot of Reversible Mastery
- AI Slop: Reading It as Outsourced Verification, Not Low Quality
- Asking an LLM for Design: Briefs, Skills, and a Process That Keep UIs from Converging on the Obvious
- Does Prototyping Widen Design Exploration? Fixation on Early Examples, Parallel Prototyping, Rapid Prototyping with Generative AI, and Documentation in Research through Design
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Rank 7
Science and AI models
AI models collapse when trained on recursively generated data ↗
Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao et al. (2024). Nature
Shows through theory and experiments with LLMs, VAEs and GMMs that training on recursively generated data causes irreversible model collapse, in which the tails of the original distribution vanish.
Cited 758 times worldwide · First cited here 2026-07-19
Cited in 6 notes
- Where Are the Gaps in AI Research? Seven Voids Found by Reframing
- AI Research Gaps, a Third Time: Rereading Them as Rewritings of the Game Board
- AI Research Gaps, Revisited: Sorting Symmetry-Completion from Coverage, and Digging Out an Endogenous Scaling Law
- Gaps in AI Research, a Fourth Time: Seen from Outside the Inversion Family
- AI Slop: Reading It as Outsourced Verification, Not Low Quality
- Does Seeming Common Online Mean It Is Common?
Cited by 5 notes (9)
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Rank 11
Generative AI and creativity
Generative artificial intelligence, human creativity, and art ↗
Eric Zhou & Dokyun Lee (2024). PNAS Nexus
Analysing over 4 million artworks from more than 50,000 users, finds that adopting text-to-image AI raised productivity by 25% and favourites per view by 50%, while average novelty declined.
Cited 450 times worldwide · First cited here 2026-06-07
Cited in 5 notes
- AI in the Design Industry: An Academic Review (2026)
- The Scholarly Lineage of Generative Art: A 94-Item Literature Map from Information Aesthetics to the Post-LLM Era
- Which Angle for a Generative-Art Review Paper Attracts Citations? An Empirical Analysis Using Bibliographic Data
- AI Slop: Reading It as Outsourced Verification, Not Low Quality
- Does Prototyping Widen Design Exploration? Fixation on Early Examples, Parallel Prototyping, Rapid Prototyping with Generative AI, and Documentation in Research through Design
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Rank 11
Design theory and methods
Nigel Cross (2006). Springer-Verlag
A book that frames designerly ways of knowing as a third culture distinct from the sciences and humanities, describing designers' distinctive cognition from protocol studies.
No abstract was available, so this summary is based on how the notes on this site describe the work.
Cited 727 times worldwide · First cited here 2026-07-09
Cited in 5 notes
- The Nexus of Design and Social Science: A Genealogy of Methods, Theories, and Institutions
- Field Deploy Engineers and Designers: How Field Knowledge Shapes Design Judgment
- Is the Primacy of Qualitative Methods in Design Research Academically Mainstream?
- Qualitative and Quantitative Research: In the Context of Design
- Adversarial Review of Two Generative Art Surveys: Examining the Literature Map and the Citation Strategy Against the Design-Theory Canon
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Rank 11
Design theory and methods
Creativity in the design process: co-evolution of problem–solution ↗
Kees Dorst & Nigel Cross (2001). Design Studies
A protocol study of nine experienced industrial designers shows that creative design involves co-evolution, with problem and solution changing together.
Cited 2,354 times worldwide · First cited here 2026-07-09
Cited in 5 notes
- Activating Reframing in Design: Cognitive Mechanisms and Practical Methods
- Adversarial Review of Two Generative Art Surveys: Examining the Literature Map and the Citation Strategy Against the Design-Theory Canon
- When Judgment Becomes a Function Call, What Moves in Design
- What Does It Take to Produce Novelty Now? Separating Traits, Attitudes, Stances, Abilities, and Skills
- Does Prototyping Widen Design Exploration? Fixation on Early Examples, Parallel Prototyping, Rapid Prototyping with Generative AI, and Documentation in Research through Design
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Rank 11
Design theory and methods
Wicked Problems in Design Thinking ↗
Richard Buchanan (1992). Design Issues
Building on Rittel and Webber's wicked problems, argues that the subject matter of design is indeterminate and discusses design thinking through four orders: signs, things, actions and thoughts.
No abstract was available, so this summary is based on how the notes on this site describe the work.
Cited 3,534 times worldwide · First cited here 2026-07-09
Cited in 5 notes
- The Nexus of Design and Social Science: A Genealogy of Methods, Theories, and Institutions
- The Epistemological Premises and Methodological Foundations of Design: A Literature Map of Wicked Problems, Abduction, and Set-Based Exploration
- Design and the Professions: A Literature Map of Design's Position Seen Through the Theory of Professions
- Adversarial Review of Two Generative Art Surveys: Examining the Literature Map and the Citation Strategy Against the Design-Theory Canon
- What Was Measured as "Gyaru-Mind": The Provenance of Eight Factors and Its Limits
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Rank 11
Design theory and methods
Sasha Costanza-Chock (2020). The MIT Press
A book that analyses power asymmetries in design processes through intersectionality, the social model of disability and community organizing, and systematizes them as the design justice framework.
No abstract was available, so this summary is based on how the notes on this site describe the work.
First cited here 2026-07-09
Cited in 5 notes
- The Nexus of Design and Social Science: A Genealogy of Methods, Theories, and Institutions
- Participatory Design and the Design of Collaboration: A Literature Map of Boundary Objects, Infrastructuring, and the Conditions of Participation
- Adversarial Review of Two Generative Art Surveys: Examining the Literature Map and the Citation Strategy Against the Design-Theory Canon
- Difference Does Not Become a Criterion: An Adversarial Review of a Novelty Claim about Gyaru Practice
- What Was Measured as "Gyaru-Mind": The Provenance of Eight Factors and Its Limits
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Rank 11
Science and AI models
Diffraction of Electrons by a Crystal of Nickel ↗
C. Davisson & L. H. Germer (1927). Physical Review
Measured the angular distribution of electrons scattered by a nickel single crystal and found diffraction beams whose wavelengths agree with h/mv of wave mechanics.
Cited 997 times worldwide · First cited here 2026-07-19
Cited in 5 notes
- Is Capability Inside the Model? A Relational Ontology of AI Evaluation and a Test for Contextuality
- AI Research Gaps, Revisited: Sorting Symmetry-Completion from Coverage, and Digging Out an Endogenous Scaling Law
- The Creativity of Subtraction: The Additive Bias, and the Hypothesis of Designing Absence
- The Vanishing of Knowledge: Undiscovery as the Dual of Discovery
- Is Expertise a One-Way Street? Unlearning and the Blind Spot of Reversible Mastery
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Rank 11
Science and AI models
Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers ↗
Chenglei Si, Diyi Yang & Tatsunori Hashimoto (2024). arXiv
A blinded study with over 100 NLP researchers found LLM-generated research ideas judged more novel than expert ideas but slightly less feasible, and noted failures of LLM self-evaluation and low diversity.
Cited 44 times worldwide · First cited here 2026-07-19
Cited in 5 notes
- Where Are the Gaps in AI Research? Seven Voids Found by Reframing
- Gaps in AI Research, a Fourth Time: Seen from Outside the Inversion Family
- Where Do Novel Research Questions Come From? The Four Loci and Their Combinations, Examined Against the Literature
- How Novelty Is Made and How It Is Claimed: A Typology of Establishing Strategies
- What Does It Take to Produce Novelty Now? Separating Traits, Attitudes, Stances, Abilities, and Skills
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Rank 11
Research questions and methods
What should we expect from research through design? ↗
William Gaver (2012). Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Drawing on philosophy of science, argues that research through design yields provisional, contingent theories and should treat theory as annotation of design portfolios rather than pursue convergence and standardisation.
Cited 1,122 times worldwide · First cited here 2026-07-27
Cited in 5 notes
- Where Do Novel Research Questions Come From? The Four Loci and Their Combinations, Examined Against the Literature
- How Novelty Is Made and How It Is Claimed: A Typology of Establishing Strategies
- Writing Material for a Review Article: The Assumption Ledger and Nearest-Neighbour Literature Left by 17 Novelty Audits
- What Was Measured as "Gyaru-Mind": The Provenance of Eight Factors and Its Limits
- Does Prototyping Widen Design Exploration? Fixation on Early Examples, Parallel Prototyping, Rapid Prototyping with Generative AI, and Documentation in Research through Design
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Rank 11
Learning, cognition and ability
Much ado about grit: A meta-analytic synthesis of the grit literature. ↗
Marcus Credé, Michael C. Tynan & Peter D. Harms (2017). Journal of Personality and Social Psychology
A meta-analysis of 88 samples (66,807 people) finding no support for grit's higher-order structure, moderate links to performance, and very strong overlap with conscientiousness; only perseverance of effort added explanatory power.
Cited 1,660 times worldwide · First cited here 2026-08-15
Cited in 5 notes
- When Is a Disposition Being Measured? Measurement as Conditional Tendency, and Measurement as Co-occurrence
- How Might Gyaru-Mind Be Measured: What the Two Existing Scales Leave Out
- Where Are the Seeds of Novelty Found? Surprise, Rereading, and Novelty That Is Only Apparent
- What Does It Take to Produce Novelty Now? Separating Traits, Attitudes, Stances, Abilities, and Skills
- 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
Cited by 4 notes (12)
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Rank 20
AI in design practice and education
Generating Automatic Feedback on UI Mockups with Large Language Models ↗
Peitong Duan, Jeremy Warner, Yang Li et al. (2024). Proceedings of the CHI Conference on Human Factors in Computing Systems
Builds a Figma plugin that uses GPT-4 to automate heuristic evaluation of UI mockups; tests on 51 UIs and with 12 designers showed it caught subtle errors and improved text, but grew less useful over iterations.
Cited 81 times worldwide · First cited here 2026-06-07
Cited in 4 notes
- AI in the Design Industry: An Academic Review (2026)
- Recent Currents in LLM-as-a-Judge: A Literature Map of 53 Core Studies and a Standalone Chapter on Creativity Evaluation
- When Judgment Becomes a Function Call, What Moves in Design
- Asking an LLM for Design: Briefs, Skills, and a Process That Keep UIs from Converging on the Obvious
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Rank 20
AI, work and the economy
Experimental evidence on the productivity effects of generative artificial intelligence ↗
Shakked Noy & Whitney Zhang (2023). Science
A preregistered experiment giving ChatGPT at random to half of 453 college-educated professionals on writing tasks found that time fell by 40%, quality rose by 18%, and inequality between workers decreased.
Cited 1,797 times worldwide · First cited here 2026-06-08
Cited in 4 notes
- Does AI Make Smart People Smarter and Everyone Else Shallower? — A Two-Lineage Debate Between Industry and Academia
- The Economics of AI and Design — Reading the Structural Transformation of Design Labor Through Six Economic Theories
- Competency Measurement Premised on AI Use — The Aptitude x AI Skill Interaction and Measurement Frameworks
- Research Currents in AI and Economics: Productivity, Labor Markets, and Methods (2024–2026)
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Rank 20
Design theory and methods
Nigel Cross (1982). Design Studies
A foundational essay establishing design as a third way of knowing, distinct from the sciences and humanities, and setting out designerly ways of knowing.
Cited 1,506 times worldwide · First cited here 2026-07-09
Cited in 4 notes
- The Nexus of Design and Social Science: A Genealogy of Methods, Theories, and Institutions
- The Many Dimensions of Design Value: A Literature Map from Instrumental to Relational and Recognitive Value
- Where Do Novel Research Questions Come From? The Four Loci and Their Combinations, Examined Against the Literature
- Adversarial Review of Two Generative Art Surveys: Examining the Literature Map and the Citation Strategy Against the Design-Theory Canon
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Rank 20
Generative AI and creativity
Syeda Masooma Naqvi, Ruichen He & Harmanpreet Kaur (2025). Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
Interviews with 28 designers at different levels of expertise show that generative AI is accepted as a collaborative tool amid debates over originality and ownership, with experienced designers worried about losing foundational skills.
No abstract was available, so this summary is based on how the notes on this site describe the work.
Cited 18 times worldwide · First cited here 2026-07-12
Cited in 4 notes
- AI Adaptation in Design Education — The Current State and Structural Challenges of Curriculum Reform
- From Maker to Editor: A Structural Analysis of the Designer Role Transition in the Age of AI
- The Scholarly Lineage of Generative Art: A 94-Item Literature Map from Information Aesthetics to the Post-LLM Era
- Which Angle for a Generative-Art Review Paper Attracts Citations? An Empirical Analysis Using Bibliographic Data
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Rank 20
Science and AI models
The Matthew Effect in Science ↗
Robert K. Merton (1968). Science
Describes the Matthew effect, whereby eminent scientists receive disproportionate credit, and argues that it also heightens the visibility of their work in scientific communication and concentrates resources and talent.
Cited 6,579 times worldwide · First cited here 2026-07-15
Cited in 4 notes
- Is the Primacy of Qualitative Methods in Design Research Academically Mainstream?
- Creativity, Situated Cognition, and Environment: A Literature Map of 4E Cognition, Affordances, and Situated Learning
- How Novelty Is Made and How It Is Claimed: A Typology of Establishing Strategies
- Do Those Who Have Receive More? Mechanisms, Causal Identification, and Limits of Cumulative Advantage and the Matthew Effect in the Academic Literature
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Rank 20
Learning, cognition and ability
Jean Lave & Etienne Wenger (1991). Cambridge University Press
Lave and Wenger view learning as a social process and propose legitimate peripheral participation, by which newcomers move toward full participation in communities of practice.
Cited 33,270 times worldwide · First cited here 2026-07-19
Cited in 4 notes
- Creativity, Situated Cognition, and Environment: A Literature Map of 4E Cognition, Affordances, and Situated Learning
- The Many Dimensions of Design Value: A Literature Map from Instrumental to Relational and Recognitive Value
- What Was Measured as "Gyaru-Mind": The Provenance of Eight Factors and Its Limits
- Can Incompetence Be Defined as Raising Other People's Cognitive Load?
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Rank 20
Science and AI models
Artificial intelligence and illusions of understanding in scientific research ↗
Lisa Messeri & M. J. Crockett (2024). Nature
Classifies four ways scientists envision AI in research and argues that they create illusions of understanding and a scientific monoculture of methods and questions that researchers themselves fail to see.
No abstract was available, so this summary is based on how the notes on this site describe the work.
Cited 677 times worldwide · First cited here 2026-07-19
Cited in 4 notes
- AI, Psychology, and Cognitive Science Research Trends: Machine Psychology and the Cognitive Modeling of LLMs (2024–2026)
- Where Are the Gaps in AI Research? Seven Voids Found by Reframing
- AI Research Gaps, a Third Time: Rereading Them as Rewritings of the Game Board
- Gaps in AI Research, a Fourth Time: Seen from Outside the Inversion Family
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Rank 20
Learning, cognition and ability
The Expertise Reversal Effect ↗
Slava Kalyuga, Paul Ayres, Paul Chandler et al. (2003). Educational Psychologist
Names the expertise reversal effect, in which instructional techniques effective for novices lose effectiveness or backfire with more experienced learners, and reviews the empirical studies behind it.
Cited 1,919 times worldwide · First cited here 2026-07-19
Cited in 4 notes
- Is Expertise a One-Way Street? Unlearning and the Blind Spot of Reversible Mastery
- The Debate Over Designing Failure Into Learning: Four Lineages Versus Cognitive Load Theory
- Does Being Able to Build Mean Being Able to Learn? Rereading the Learning Outcomes of End-User Development Through Motive
- Can Incompetence Be Defined as Raising Other People's Cognitive Load?
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Rank 20
Learning, cognition and ability
AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking ↗
Michael Gerlich (2025). Societies
A mixed-methods study of 666 participants found a negative correlation between frequent AI tool use and critical thinking, mediated by cognitive offloading, with younger people more dependent on AI.
Cited 1,056 times worldwide · First cited here 2026-07-19
Cited in 4 notes
- Research Currents in AI, Education, and the Learning Sciences: Evidence on Generative AI and Learning (2024–2026)
- AI, Psychology, and Cognitive Science Research Trends: Machine Psychology and the Cognitive Modeling of LLMs (2024–2026)
- Where Are the Gaps in AI Research? Seven Voids Found by Reframing
- Does Early Use of Generative AI Inhibit the Formation of Thought? A Literature Map of Cognitive Offloading and Learning
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Rank 20
Research questions and methods
Generating Research Questions Through Problematization ↗
Mats Alvesson & Jörgen Sandberg (2011). Academy of Management Review
Proposes problematization, identifying and challenging the assumptions underlying existing literature, as a methodology for generating research questions likely to lead to more influential theories than gap-spotting.
Cited 648 times worldwide · First cited here 2026-07-19
Cited in 4 notes
- AI Research Gaps, a Third Time: Rereading Them as Rewritings of the Game Board
- Thinking in Problem Definition: Rereading the Game Board of Existing Research
- Where Do Novel Research Questions Come From? The Four Loci and Their Combinations, Examined Against the Literature
- Writing Material for a Review Article: The Assumption Ledger and Nearest-Neighbour Literature Left by 17 Novelty Audits
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Rank 20
Science and AI models
Matthias Gerstgrasser, Rylan Schaeffer, Apratim Dey et al. (2024). arXiv
Experiments and theory show that replacing real data with each generation's synthetic data leads to model collapse, whereas accumulating synthetic data alongside real data avoids it and keeps test error bounded.
Cited 12 times worldwide · First cited here 2026-07-19
Cited in 4 notes
- Where Are the Gaps in AI Research? Seven Voids Found by Reframing
- AI Research Gaps, a Third Time: Rereading Them as Rewritings of the Game Board
- AI Research Gaps, Revisited: Sorting Symmetry-Completion from Coverage, and Digging Out an Endogenous Scaling Law
- Does Seeming Common Online Mean It Is Common?
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Rank 20
Learning, cognition and ability
Paul A. Kirschner, John Sweller & Richard E. Clark (2006). Educational Psychologist
Drawing on cognitive architecture, expert–novice differences and cognitive load, argues that minimally guided instruction is less effective and efficient than guided instruction, except for learners with high prior knowledge.
Cited 6,881 times worldwide · First cited here 2026-08-07
Cited in 4 notes
- Designing Failure Into Learning: From Expectation Failure to Productive Failure and Industry Implementations
- The Debate Over Designing Failure Into Learning: Four Lineages Versus Cognitive Load Theory
- Issues for Design Education (Not Craft Education): From the Evidence on Failure Design and Cognitive Offloading
- Does Being Able to Build Mean Being Able to Learn? Rereading the Learning Outcomes of End-User Development Through Motive
Cited by 3 notes (19)
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Rank 32
AI in design practice and education
Graphic Design Education in the Era of Text‐to‐Image Generation: Transitioning to Contents Creator ↗
Younjung Hwang & Yi Wu (2025). International Journal of Art & Design Education
Analysing a course in which students used Midjourney and DALL-E to make posters on European design history, argues that graphic design education now needs AI visual literacy and stronger preliminary research.
Cited 46 times worldwide · First cited here 2026-06-07
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Rank 32
Generative AI and creativity
Sangho Suh, Meng Chen, Bryan Min et al. (2024). Proceedings of the CHI Conference on Human Factors in Computing Systems
Arguing that LLM interfaces tend to push users toward early convergence on a few ideas, the authors built Luminate, which first generates task-relevant dimensions and values and then lays outputs along them for exploration, comparison, and synthesis. In a user study with 14 professional writers, support for exploration received the highest ratings.
Cited 145 times worldwide · First cited here 2026-06-07
Cited in 3 notes
- AI in the Design Industry: An Academic Review (2026)
- Asking an LLM for Design: Briefs, Skills, and a Process That Keep UIs from Converging on the Obvious
- Does Prototyping Widen Design Exploration? Fixation on Early Examples, Parallel Prototyping, Rapid Prototyping with Generative AI, and Documentation in Research through Design
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Rank 32
AI in design practice and education
User Experience Design Professionals’ Perceptions of Generative Artificial Intelligence ↗
Jie Li, Hancheng Cao, Laura Lin et al. (2024). Proceedings of the CHI Conference on Human Factors in Computing Systems
Interviews with 20 UX designers found that experienced designers saw GenAI as assistive and trusted their own creativity, whereas junior designers faced risks of skill degradation, job replacement and creative exhaustion.
Cited 150 times worldwide · First cited here 2026-06-26
Cited in 3 notes
- The Academic Foundations of Agentic Experience (AX): Where Human-Agent Interaction Research Stands
- Designer Careers That Will Generate Value over the Next Five Years — Academic Review (2026)
- Writing Material for a Review Article: The Assumption Ledger and Nearest-Neighbour Literature Left by 17 Novelty Audits
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Rank 32
AI, work and the economy
AI and work in the creative industries: digital continuity or discontinuity? ↗
Kristofer Erickson (2024). Creative Industries Journal
Case studies of six commercial AI products show they were more labour-intensive than traditional media products, combining production and computational skills, while human contributions became invisible in the final products.
Cited 63 times worldwide · First cited here 2026-06-28
Cited in 3 notes
- Designer Careers That Will Generate Value over the Next Five Years — Academic Review (2026)
- EU AI Act Article 50 and Design Practice — A Structural Analysis on the Eve of Enforcement
- Research Currents in AI and the Study of Art and Culture: Aesthetics, Media Studies, Computational Creativity (2024–2026)
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Rank 32
AI, work and the economy
GPTs are GPTs: Labor market impact potential of LLMs ↗
Tyna Eloundou, Sam Manning, Pamela Mishkin et al. (2024). Science
Scoring O*NET occupational tasks with an exposure rubric, estimates that about 80% of US occupations have at least 10% of tasks exposed to LLMs such as GPT-4, with higher-wage jobs more exposed.
No abstract was available, so this summary is based on how the notes on this site describe the work.
Cited 770 times worldwide · First cited here 2026-06-28
Cited in 3 notes
- The Economics of AI and Design — Reading the Structural Transformation of Design Labor Through Six Economic Theories
- Research Currents in AI and Economics: Productivity, Labor Markets, and Methods (2024–2026)
- AI and Social Science Research Trends: Computational Social Science and LLM Agents (2024–2026)
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Rank 32
AI in design practice and education
Design Principles for Generative AI Applications ↗
Justin D. Weisz, Jessica He, Michael Muller et al. (2024). Proceedings of the CHI Conference on Human Factors in Computing Systems
Through literature review, practitioner feedback, validation against real applications and use in two products, develops six design principles for generative AI applications, each paired with design strategies.
Cited 224 times worldwide · First cited here 2026-06-28
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Rank 32
AI, work and the economy
Automation and New Tasks: How Technology Displaces and Reinstates Labor ↗
Daron Acemoglu & Pascual Restrepo (2019). Journal of Economic Perspectives
Acemoglu and Restrepo frame technology's labor effects by task allocation: automation's displacement effect lowers the labor share, while new tasks' reinstatement effect raises labor share and demand, and apply this to US employment.
Cited 2,558 times worldwide · First cited here 2026-06-28
Cited in 3 notes
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Rank 32
AI, work and the economy
Xiang Hui, Oren Reshef & Luofeng Zhou (2024). Organization Science
After ChatGPT and image generators launched, freelancers in highly exposed occupations on a large platform lost jobs and earnings; past performance did not buffer this, and top freelancers may have been hit harder.
Cited 186 times worldwide · First cited here 2026-06-28
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Rank 32
AI, work and the economy
Fabrizio Dell’Acqua, Edward McFowland, Ethan Mollick et al. (2026). Organization Science
In a preregistered experiment with 758 BCG consultants, GPT-4 raised task completion 12.2% and speed 25.1% inside AI's capability frontier but cut correctness 19% outside it, a pattern the authors call the jagged technological frontier.
Cited 174 times worldwide · First cited here 2026-06-28
Cited in 3 notes
- The Economics of AI and Design — Reading the Structural Transformation of Design Labor Through Six Economic Theories
- What Is the Frontier Model Premium Buying? (A Debate via Historical Analogy)
- Competency Measurement Premised on AI Use — The Aptitude x AI Skill Interaction and Measurement Frameworks
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Rank 32
AI, work and the economy
The Impact of AI on Developer Productivity: Evidence from GitHub Copilot ↗
Sida Peng, Eirini Kalliamvakou, Peter Cihon et al. (2023). arXiv
In a controlled experiment in which developers implemented an HTTP server in JavaScript, those with GitHub Copilot finished 55.8% faster than the control group.
Cited 279 times worldwide · First cited here 2026-07-03
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Rank 32
Design theory and methods
C-K design theory: an advanced formulation ↗
Armand Hatchuel & Benoit Weil (2008). Research in Engineering Design
Formalizes design as the joint expansion of a Concept space and a Knowledge space, treating propositions undecidable with existing knowledge as concepts at the core of problem setting.
Cited 699 times worldwide · First cited here 2026-07-09
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Rank 32
Design theory and methods
Collaborative problem–solution co-evolution in creative design ↗
Stefan Wiltschnig, Bo T. Christensen & Linden J. Ball (2013). Design Studies
A qualitative protocol analysis of design teams showing that episodes of problem–solution co-evolution are coupled with analogy and mental simulation.
Cited 185 times worldwide · First cited here 2026-07-09
Cited in 3 notes
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Rank 32
Design theory and methods
The reflective practice of design teams ↗
Rianne Valkenburg & Kees Dorst (1998). Design Studies
Applying Schön's reflective practice to design teams, the authors use observation protocols to describe shared frame formation and the framing to reflecting cycle.
Cited 417 times worldwide · First cited here 2026-07-09
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Rank 32
Design theory and methods
Co-creation and the new landscapes of design ↗
Elizabeth B.-N. Sanders & Pieter Jan Stappers (2008). CoDesign
Reviews the shift from user-centred design to co-designing and argues that it changes the roles of designer, researcher and the former 'user', as well as design education.
Cited 5,311 times worldwide · First cited here 2026-07-09
Cited in 3 notes
- Activating Reframing in Design: Cognitive Mechanisms and Practical Methods
- Creativity, Situated Cognition, and Environment: A Literature Map of 4E Cognition, Affordances, and Situated Learning
- Justifying the Democratization of Design: A Literature Map of Public Goods, the Capability Approach, and Theories of Justice
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Rank 32
Design theory and methods
Rethinking Design Thinking: Part I ↗
Lucy Kimbell (2011). Design and Culture
Reviews how 'design thinking' spread from design research to management, identifies three accounts (cognitive style, general theory, organizational resource), critiques them, and proposes attending to designers' situated, embodied practices.
Cited 962 times worldwide · First cited here 2026-07-09
Cited in 3 notes
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Rank 32
AI in design practice and education
Rodrigo Hernández-Ramírez & João Batalheiro Ferreira (2024). She Ji: The Journal of Design, Economics, and Innovation
A critical essay arguing that fears of GenAI ending design work stem from managerialism's view of design as procedure, and that design is craft-based knowledge work that cannot be automated.
Cited 8 times worldwide · First cited here 2026-07-12
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Rank 32
AI, work and the economy
Generative-AI, the media industries, and the disappearance of human creative labour ↗
Stuart Bender (2024). Media Practice and Education
Critiques the 'replacing tasks' model in the discourse of the 2023 Writers' and Actors' strikes and uses a meaningful-work framework to argue for human–GenAI coexistence that amplifies human creativity.
Cited 53 times worldwide · First cited here 2026-07-12
Cited in 3 notes
- From Maker to Editor: A Structural Analysis of the Designer Role Transition in the Age of AI
- Research Currents in AI and the Study of Art and Culture: Aesthetics, Media Studies, Computational Creativity (2024–2026)
- The Scholarly Lineage of Generative Art: A 94-Item Literature Map from Information Aesthetics to the Post-LLM Era
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Rank 32
AI in design practice and education
Tracing the Invisible: Understanding Students’ Judgment in AI-Supported Design Work ↗
Suchismita Naik, Prakash Shukla, Ike Obi et al. (2025). Proceedings of the 2025 Conference on Creativity and Cognition
Analysing reflections from 33 student teams in an HCI design course, identifies established design judgments plus two new types arising with generative AI: agency-distribution judgment and reliability judgment.
Cited 1 times worldwide · First cited here 2026-07-12
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Rank 32
AI in design practice and education
Samangi Wadinambiarachchi, Jenny Waycott & Greg Wadley (2026). Proceedings of the 2026 Conference on Creativity and Cognition
The authors built SketchifAI, which accepts sketches as prompts in AI-supported ideation, and compared text, sketch, and sketch-plus-tag input with design students. Sketch input tended to increase fluency, differences in variety and originality were inconclusive, and students strongly preferred text prompts.
Cited 0 times worldwide · First cited here 2026-07-12
Cited in 3 notes
- AI Adaptation in Design Education — The Current State and Structural Challenges of Curriculum Reform
- AI and Design Monthly Scholarly Watch (July–August 2026)
- Does Prototyping Widen Design Exploration? Fixation on Early Examples, Parallel Prototyping, Rapid Prototyping with Generative AI, and Documentation in Research through Design
How the counts are made
- Only published notes are counted. Drafts are not counted, and neither are the English notes (they translate the Japanese ones).
- Papers are counted from the reference section of each note: anything with a DOI, plus papers on arXiv, the ACL Anthology, and OpenReview. Books without a DOI, web pages, and reports are not counted.
- A note citing the same paper more than once counts once.
- Title, authors, year, and venue come from the DOI registration. Worldwide citation counts are from OpenAlex (as of 2026-10-09).
- Each summary was written from the paper's abstract or from the annotated source list made while writing the note. Abstracts are not reproduced.