Difference between revisions of "AI and Humans"
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** [https://arxiv.org/abs/2410.03017 Tutor CoPilot: A Human-AI Approach for Scaling Real-Time Expertise] | ** [https://arxiv.org/abs/2410.03017 Tutor CoPilot: A Human-AI Approach for Scaling Real-Time Expertise] | ||
* 2025-06: Gallup & The Walton Foundation: [https://www.gallup.com/file/analytics/691922/Walton-Family-Foundation-Gallup-Teachers-AI-Report.pdf Teaching for Tomorrow Unlocking Six Weeks a Year With AI] | * 2025-06: Gallup & The Walton Foundation: [https://www.gallup.com/file/analytics/691922/Walton-Family-Foundation-Gallup-Teachers-AI-Report.pdf Teaching for Tomorrow Unlocking Six Weeks a Year With AI] | ||
| + | * 2026-03: [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6423358 Effective Personalized AI Tutors via LLM-Guided Reinforcement Learning] | ||
==AI harms learning== | ==AI harms learning== | ||
| Line 101: | Line 102: | ||
* 2024-09: [https://docs.iza.org/dp17302.pdf Creative and Strategic Capabilities of Generative AI: Evidence from Large-Scale Experiments] | * 2024-09: [https://docs.iza.org/dp17302.pdf Creative and Strategic Capabilities of Generative AI: Evidence from Large-Scale Experiments] | ||
* 2025-06: [https://arxiv.org/abs/2506.00794 Predicting Empirical AI Research Outcomes with Language Models] | * 2025-06: [https://arxiv.org/abs/2506.00794 Predicting Empirical AI Research Outcomes with Language Models] | ||
| + | * 2026-03: [https://arxiv.org/abs/2603.19087 Serendipity by Design: Evaluating the Impact of Cross-domain Mappings on Human and LLM Creativity] | ||
===Art=== | ===Art=== | ||
| Line 171: | Line 173: | ||
* 2025-10: [https://arxiv.org/abs/2510.12049 Generative AI and Firm Productivity: Field Experiments in Online Retail] | * 2025-10: [https://arxiv.org/abs/2510.12049 Generative AI and Firm Productivity: Field Experiments in Online Retail] | ||
* 2025-10: Wharton: [https://knowledge.wharton.upenn.edu/special-report/2025-ai-adoption-report/ 2025 AI Adoption Report] (75% report positive ROI) | * 2025-10: Wharton: [https://knowledge.wharton.upenn.edu/special-report/2025-ai-adoption-report/ 2025 AI Adoption Report] (75% report positive ROI) | ||
| + | * 2026-03: [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6513481 Mapping AI into Production: A Field Experiment on Firm Performance] | ||
===Coding=== | ===Coding=== | ||
| Line 194: | Line 197: | ||
* 2026-01: [https://www.anthropic.com/news/accelerating-scientific-research How scientists are using Claude to accelerate research and discovery] | * 2026-01: [https://www.anthropic.com/news/accelerating-scientific-research How scientists are using Claude to accelerate research and discovery] | ||
* 2026-02: [https://arxiv.org/abs/2602.03837 Accelerating Scientific Research with Gemini: Case Studies and Common Techniques] | * 2026-02: [https://arxiv.org/abs/2602.03837 Accelerating Scientific Research with Gemini: Case Studies and Common Techniques] | ||
| + | * 2026-03: [https://www.pnas.org/doi/10.1073/pnas.2533676123 Expert evaluation of LLM world models: A high-Tc superconductivity case study] ([https://research.google/blog/testing-llms-on-superconductivity-research-questions/?utm_source=twitter&utm_medium=social&utm_campaign=social_post&utm_content=gr-acct blog]) | ||
===Medical=== | ===Medical=== | ||
| Line 316: | Line 320: | ||
* 2025-12: [https://www.edelman.com/sites/g/files/aatuss191/files/2025-11/2025%20Edelman%20Trust%20Barometer%20Flash%20Poll%20Trust%20and%20Artificial%20Intelligence%20at%20a%20Crossroads%201.pdf 2025 Edelman Trust Barometer] | * 2025-12: [https://www.edelman.com/sites/g/files/aatuss191/files/2025-11/2025%20Edelman%20Trust%20Barometer%20Flash%20Poll%20Trust%20and%20Artificial%20Intelligence%20at%20a%20Crossroads%201.pdf 2025 Edelman Trust Barometer] | ||
* 2025-12: [https://navigatorresearch.org/views-of-ai-and-data-centers/ Polling - Views of AI and data centers] | * 2025-12: [https://navigatorresearch.org/views-of-ai-and-data-centers/ Polling - Views of AI and data centers] | ||
| + | * 2026-03: [https://osf.io/preprints/psyarxiv/5mwre_v9 The Moralization of Artificial Intelligence] | ||
| + | * 2026-03: Anthropic: [https://www.anthropic.com/features/81k-interviews What 81,000 people want from AI] | ||
==AI Persuasion of Humans== | ==AI Persuasion of Humans== | ||
| Line 326: | Line 332: | ||
* 2025-04: [https://drive.google.com/file/d/1Eo4SHrKGPErTzL1t_QmQhfZGU27jKBjx/edit Can AI Change Your View? Evidence from a Large-Scale Online Field Experiment] | * 2025-04: [https://drive.google.com/file/d/1Eo4SHrKGPErTzL1t_QmQhfZGU27jKBjx/edit Can AI Change Your View? Evidence from a Large-Scale Online Field Experiment] | ||
** [https://www.404media.co/researchers-secretly-ran-a-massive-unauthorized-ai-persuasion-experiment-on-reddit-users/ Researchers Secretly Ran a Massive, Unauthorized AI Persuasion Experiment on Reddit Users] | ** [https://www.404media.co/researchers-secretly-ran-a-massive-unauthorized-ai-persuasion-experiment-on-reddit-users/ Researchers Secretly Ran a Massive, Unauthorized AI Persuasion Experiment on Reddit Users] | ||
| + | * 2025-05: [https://www.nature.com/articles/s41562-025-02194-6 On the conversational persuasiveness of GPT-4] | ||
* 2025-05: [https://arxiv.org/abs/2505.09662 Large Language Models Are More Persuasive Than Incentivized Human Persuaders] | * 2025-05: [https://arxiv.org/abs/2505.09662 Large Language Models Are More Persuasive Than Incentivized Human Persuaders] | ||
* 2025-07: [https://arxiv.org/abs/2507.13919 The Levers of Political Persuasion with Conversational AI] | * 2025-07: [https://arxiv.org/abs/2507.13919 The Levers of Political Persuasion with Conversational AI] | ||
| − | * 2025-12: [https://www.science.org/doi/10.1126/science.aea3884 The levers of political persuasion with conversational artificial intelligence] | + | ** 2025-12: [https://www.science.org/doi/10.1126/science.aea3884 The levers of political persuasion with conversational artificial intelligence] |
* 2025-12: [https://www.nature.com/articles/s41586-025-09771-9 Persuading voters using human–artificial intelligence dialogues] | * 2025-12: [https://www.nature.com/articles/s41586-025-09771-9 Persuading voters using human–artificial intelligence dialogues] | ||
| Line 336: | Line 343: | ||
* 2025-03: [https://cdn.openai.com/papers/15987609-5f71-433c-9972-e91131f399a1/openai-affective-use-study.pdf Investigating Affective Use and Emotional Well-being on ChatGPT] | * 2025-03: [https://cdn.openai.com/papers/15987609-5f71-433c-9972-e91131f399a1/openai-affective-use-study.pdf Investigating Affective Use and Emotional Well-being on ChatGPT] | ||
* 2025-03: [https://dam-prod2.media.mit.edu/x/2025/03/21/Randomized_Control_Study_on_Chatbot_Psychosocial_Effect.pdf How AI and Human Behaviors Shape Psychosocial Effects of Chatbot Use: A Longitudinal Controlled Study] | * 2025-03: [https://dam-prod2.media.mit.edu/x/2025/03/21/Randomized_Control_Study_on_Chatbot_Psychosocial_Effect.pdf How AI and Human Behaviors Shape Psychosocial Effects of Chatbot Use: A Longitudinal Controlled Study] | ||
| + | * 2026-03: [https://arxiv.org/abs/2603.15245 Practicing with Language Models Cultivates Human Empathic Communication] | ||
===Counter loneliness=== | ===Counter loneliness=== | ||
Latest revision as of 20:57, 4 April 2026
Contents
AI in Education
Survey/study of
- 2023-08: Perception, performance, and detectability of conversational artificial intelligence across 32 university courses
- 2023-10: Employees secretly using AI at work.
- 2023-10: Survey shows students using AI more than professors.
- 2023-11: ChatGPT has entered the classroom: how LLMs could transform education
- 2025-04: Anthropic Education Report: How University Students Use Claude
- 2025-05: The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis
AI improves learning/education
- Mollick, Ethan R. and Mollick, Lilach and Bach, Natalie and Ciccarelli, LJ and Przystanski, Ben and Ravipinto, Daniel, AI Agents and Education: Simulated Practice at Scale (June 17, 2024). The Wharton School Research Paper. doi: 10.2139/ssrn.4871171
- Can enable personalized education.
- Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutors
- GPT4 can out-perform human tutors.
- Keppler, Samantha and Sinchaisri, Wichinpong and Snyder, Clare, Backwards Planning with Generative AI: Case Study Evidence from US K12 Teachers (August 13, 2024). doi: 10.2139/ssrn.4924786
- Teachers benefit from using AI as a co-pilot to aid in tasks (planning, how to teach topic, explore ideas).
- There is smaller utility in using AI purely as a text-generator (to make quizzes, workbooks, etc.).
- Effective and Scalable Math Support: Evidence on the Impact of an AI- Tutor on Math Achievement in Ghana
- AI Tutoring Outperforms Active Learning
- From chalkboards to chatbots: Transforming learning in Nigeria, one prompt at a time (writeup)
- 6 weeks of after-school AI tutoring = 2 years of typical learning gains
- outperforms 80% of other educational interventions
- AI Meets the Classroom: When Do Large Language Models Harm Learning?
- Outcomes depend on usage
- LLM Support for Tutors GPT-4 boosts remote tutors’ performance in real time, study finds
- 2025-06: Gallup & The Walton Foundation: Teaching for Tomorrow Unlocking Six Weeks a Year With AI
- 2026-03: Effective Personalized AI Tutors via LLM-Guided Reinforcement Learning
AI harms learning
- A real-world test of artificial intelligence infiltration of a university examinations system: A “Turing Test” case study
- Current grading systems cannot detect AI.
- Bastani, Hamsa and Bastani, Osbert and Sungu, Alp and Ge, Haosen and Kabakcı, Özge and Mariman, Rei, Generative AI Can Harm Learning (July 15, 2024). The Wharton School Research Paper.doi: 10.2139/ssrn.4895486
- Access to ChatGPT harmed math education outcomes.
- 2024-09: AI Meets the Classroom: When Does ChatGPT Harm Learning?
- 2026-01: How AI Impacts Skill Formation
Software/systems
- GPTutor (code)
- EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education
- Eureka Labs (founded by Andrej Karpathy) aims to create AI-driven courses (first course is Intro to LLMs)
LLMs
Individual tools
- Chatbot (OpenAI ChatGPT, Anthropic Claude, Google Gemini)
- NotebookLM: Enables one to "chat with documents".
- Google Learn About
Systems
AI for grading
- Can Large Language Models Make the Grade? An Empirical Study Evaluating LLMs Ability To Mark Short Answer Questions in K-12 Education (preprint)
Detection
- 2024-06: Do teachers spot AI? Evaluating the detectability of AI-generated texts among student essays
- GenAI can simulate student writing in a way that teachers cannot detect.
- AI essays are assessed more positively than student-written.
- Teachers are overconfident in their source identification.
- Both novice and experienced teachers could not identify texts generated by ChatGPT vs. students
- 2025-01: People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text
AI Text Detectors Don't Work
- 2024-05: RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors
- 2024-06: Testing of Detection Tools for AI-Generated Text
AI/human
Capabilities
Writing
- 2022-12: Re3: Generating Longer Stories With Recursive Reprompting and Revision
- 2023-03: English essays: Artificial intelligence (AI) technology in OpenAI ChatGPT application: A review of ChatGPT in writing English essay
- 2023-01: Journalism: Collaborating With ChatGPT: Considering the Implications of Generative Artificial Intelligence for Journalism and Media Education
- 2023-07: Science writing: Artificial intelligence in scientific writing: a friend or a foe?
- 2024-02: Wikipedia style: Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models
- 2024-02: LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs (code)
- 2024-08: Scientific papers: [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery]
- 2024-09: PaperQA2: Language Models Achieve Superhuman Synthesis of Scientific Knowledge (𝕏 post, code)
- 2025-03: WikiAutoGen: Towards Multi-Modal Wikipedia-Style Article Generation
- 2025-03: Learning to Reason for Long-Form Story Generation
AI out-performs humans
Tests
- 2023-07: SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models
- 2024-06: A real-world test of artificial intelligence infiltration of a university examinations system: A “Turing Test” case study
- AI scores higher than median students.
Creativity
- See also: AI creativity
- 2023-07: Ideas Are Dimes A Dozen: Large Language Models For Idea Generation In Innovation
- 2023-09: Best humans still outperform artificial intelligence in a creative divergent thinking task
- Best humans out-perform AI at creativity. (By implication, median humans may not.)
- 2024-02: The current state of artificial intelligence generative language models is more creative than humans on divergent thinking tasks
- 2024-02: Felin, Teppo and Holweg, Matthias, Theory Is All You Need: AI, Human Cognition, and Causal Reasoning (February 24, 2024). doi: 10.2139/ssrn.4737265
- Argues that human "theory-based" creativity is better than AI "data-based".
- 2024-07: Pron vs Prompt: Can Large Language Models already Challenge a World-Class Fiction Author at Creative Text Writing?
- Top human (professional author) out-performs GPT4.
- 2024-09: Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers
- LLMs can be creative
- 2024-09: Creative and Strategic Capabilities of Generative AI: Evidence from Large-Scale Experiments
- 2025-06: Predicting Empirical AI Research Outcomes with Language Models
- 2026-03: Serendipity by Design: Evaluating the Impact of Cross-domain Mappings on Human and LLM Creativity
Art
- 2024-11: AI-generated poetry is indistinguishable from human-written poetry and is rated more favorably
- 2024-11: How Did You Do On The AI Art Turing Test?
Business & Marketing
- 2023-11: The power of generative marketing: Can generative AI create superhuman visual marketing content?
- 2024-02: Generative Artificial Intelligence and Evaluating Strategic Decisions
Professions
- Humanity's Last Exam
- Effort to build a dataset of challenging (but resolvable) questions in specific domain areas, to act as a benchmark to test whether AIs are improving in these challenging topics.
Coding
Medical
- 2024-03: Influence of a Large Language Model on Diagnostic Reasoning: A Randomized Clinical Vignette Study
- GPT4 improves medical practitioner work; surprisingly, GPT4 alone scored better than a human with GPT4 as aid (on selected tasks).
- 2024-10: Perspectives on Artificial Intelligence–Generated Responses to Patient Messages
- 2024-10: Large Language Model Influence on Diagnostic Reasoning; A Randomized Clinical Trial
- Use of ChatGPT does not strongly improve medical expert work; but AI alone out-scores human or human+AI
- 2024-11: Large language models surpass human experts in predicting neuroscience results (writeup: AI can predict neuroscience study results better than human experts, study finds)
- 2024-12: Superhuman performance of a large language model on the reasoning tasks of a physician
- 2024-12: HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs
- 2025-02: Media:
- 2025-02: GPT-4 assistance for improvement of physician performance on patient care tasks: a randomized controlled trial
- 2025-02: Artificial intelligence for individualized treatment of persistent atrial fibrillation: a randomized controlled trial
- Google AI Clinician:
- 2024-01: Towards Conversational Diagnostic AI (blog: Articulate Medical Intelligence Explorer, AMIE)
- 2025-03: Towards Conversational AI for Disease Management (blog)
- 2025-02: Med-RLVR: Emerging Medical Reasoning from a 3B base model via reinforcement Learning
- 2025-03: Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models
- 2025-04: Comparison of Initial Artificial Intelligence (AI) and Final Physician Recommendations in AI-Assisted Virtual Urgent Care Visits
- 2025-04: Towards conversational diagnostic artificial intelligence
- 2025-04: Towards accurate differential diagnosis with large language models
- 2025-06: Automation of Systematic Reviews with Large Language Models
- 2025-06: The Path to Medical Superintelligence
- 2025-08: A personal health large language model for sleep and fitness coaching
- 2025-08: Capabilities of GPT-5 on Multimodal Medical Reasoning
Bio
Therapy
- 2025-02: When ELIZA meets therapists: A Turing test for the heart and mind
- 2025-03: Therabot: Randomized Trial of a Generative AI Chatbot for Mental Health Treatment
Financial
HR
AI improves human work
- 2023-07: Experimental evidence on the productivity effects of generative artificial intelligence
- 2023-09: Dell'Acqua, Fabrizio and McFowland III, Edward and Mollick, Ethan R. and Lifshitz-Assaf, Hila and Kellogg, Katherine and Rajendran, Saran and Krayer, Lisa and Candelon, François and Lakhani, Karim R., Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality (September 15, 2023). Harvard Business School Technology & Operations Mgt. Unit Working Paper No. 24-013, The Wharton School Research Paper doi: 10.2139/ssrn.4573321
- 2023-11: Generative AI at Work (National Bureau of Economic Research)
- 2023-12: The Uneven Impact of Generative AI on Entrepreneurial Performance (doi: 10.31219/osf.io/hdjpk)
- 2023-12: Artificial Intelligence in the Knowledge Economy: Non-autonomous AI (chatbot) benefits least knowledgeable workers; autonomous agents benefit the most knowledgeable workers
- 2024-07: Generative AI in Real-World Workplaces: The Second Microsoft Report on AI and Productivity Research
- 2025-03: The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise
- 2025-03: Ethan Mollick: Cybernetic Teammate: Having an AI on your team can increase performance, provide expertise, and improve your experience
- 2025-09: Quantifying Human-AI Synergy
- 2025-10: Generative AI and Firm Productivity: Field Experiments in Online Retail
- 2025-10: Wharton: 2025 AI Adoption Report (75% report positive ROI)
- 2026-03: Mapping AI into Production: A Field Experiment on Firm Performance
Coding
- 2023-02: The Impact of AI on Developer Productivity: Evidence from GitHub Copilot
- 2024-09: Cui, Zheyuan and Demirer, Mert and Jaffe, Sonia and Musolff, Leon and Peng, Sida and Salz, Tobias, The Effects of Generative AI on High Skilled Work: Evidence from Three Field Experiments with Software Developers (September 03, 2024). doi: 10.2139/ssrn.4945566
- 2024-11: Hoffmann, Manuel and Boysel, Sam and Nagle, Frank and Peng, Sida and Xu, Kevin, Generative AI and the Nature of Work (October 27, 2024). Harvard Business School Strategy Unit Working Paper No. 25-021, Harvard Business Working Paper No. No. 25-021, doi: 10.2139/ssrn.5007084
- 2025-07: METR: Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity (AI tools led to lower performance)
- 2026-02: We are Changing our Developer Productivity Experiment Design (AI tools now lead to improved performance)
- 2025-09: Intuition to Evidence: Measuring AI's True Impact on Developer Productivity
Forecasting
Finance
- 2024-12: AI, Investment Decisions, and Inequality: Novices see improvements in investment performance, sophisticated investors see even greater improvements.
Law
Science
- 2025-12: Scientific production in the era of large language models
- 2026-01: Artificial intelligence tools expand scientists’ impact but contract science’s focus
- 2026-01: How scientists are using Claude to accelerate research and discovery
- 2026-02: Accelerating Scientific Research with Gemini: Case Studies and Common Techniques
- 2026-03: Expert evaluation of LLM world models: A high-Tc superconductivity case study (blog)
Medical
- 2025-03: Medical Hallucination in Foundation Models and Their Impact on Healthcare
- 2025-03: ChatGPT’s role in alleviating anxiety in total knee arthroplasty consent process: a randomized controlled trial pilot study
- 2025-05: Introducing HealthBench
- 2025-06: From Tool to Teammate: A Randomized Controlled Trial of Clinician-AI Collaborative Workflows for Diagnosis
- 2025-06: Iteratively refined ChatGPT outperforms clinical mentors in generating high-quality interprofessional education clinical scenarios: a comparative study
- 2025-07: AI-based Clinical Decision Support for Primary Care: A Real-World Study (blog)
- 2025-07: Towards physician-centered oversight of conversational diagnostic AI
- 2026-01: Interval cancer, sensitivity, and specificity comparing AI-supported mammography screening with standard double reading without AI in the MASAI study: a randomised, controlled, non-inferiority, single-blinded, population-based, screening-accuracy trial
Translation
Customer service
- 2023-11: Generative AI at Work: Improvements for workers and clients (though also a ceiling to improvement)
Creativity
- See also: AI creativity
- 2024-02: Prompting Diverse Ideas: Increasing AI Idea Variance
- 2024-07: Generative AI enhances individual creativity but reduces the collective diversity of novel content
- 2024-08: An empirical investigation of the impact of ChatGPT on creativity
- 2024-08: Response: ChatGPT decreases idea diversity in brainstorming (pdf)
- 2025-05: Response: Reply to: ChatGPT decreases idea diversity in brainstorming
- 2024-08: The Crowdless Future? Generative AI and Creative Problem-Solving
- 2024-10: Human Creativity in the Age of LLMs
- 2024-11:
Artificial Intelligence, Scientific Discovery, and Product Innovation: diffusion model increases "innovation" (patents), boosts the best performers, but also removes some enjoyable tasks.- 2025-05: Retraction: Assuring an accurate research record
- 2024-12: Using AI to Generate Visual Art: Do Individual Differences in Creativity Predict AI-Assisted Art Quality? (preprint): shows that more creative humans produce more creative genAI outputs
- 2025-01: One Does Not Simply Meme Alone: Evaluating Co-Creativity Between LLMs and Humans in the Generation of Humor
- 2025-05: Generative AI and Creativity: A Systematic Literature Review and Meta-Analysis
Equity
AI worse than humans
- 2025-04: How Good is AI at Twisting Arms? Experiments in Debt Collection
- 2025-04: Clinical knowledge in LLMs does not translate to human interactions
- 2025-05: Generalization bias in large language model summarization of scientific research
AI lowers human capability
- 2025-07: METR: Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity (blog, commentary/analysis)
- 2026-02: We are Changing our Developer Productivity Experiment Design (AI tools now lead to improved performance)
- 2026-02: Thinking—Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender
Human Perceptions of AI
- 2023-09: AI and science: what 1,600 researchers think. A Nature survey finds that scientists are concerned, as well as excited, by the increasing use of artificial-intelligence tools in research.
- 2024-11: Attitudes and perceptions of medical researchers towards the use of artificial intelligence chatbots in the scientific process: an international cross-sectional survey (Nature commentary: Quest for AI literacy)
- 2025-03: Users Favor LLM-Generated Content -- Until They Know It's AI
AI passes Turing Test
Text Dialog
- 2023-05: Human or Not? A Gamified Approach to the Turing Test
- 2023-10: Does GPT-4 pass the Turing test?
- 2024-05: People cannot distinguish GPT-4 from a human in a Turing test
- 2024-07: GPT-4 is judged more human than humans in displaced and inverted Turing tests
- 2025-03: Large Language Models Pass the Turing Test
- 2025-04: A Minimal Turing Test
Art
- 2024-11: How Did You Do On The AI Art Turing Test? Differentiation was only slightly above random (60%). AI art was often ranked higher than human-made.
- 2024-11: AI-generated poetry is indistinguishable from human-written poetry and is rated more favorably
- 2025-09: Echoes of Humanity: Exploring the Perceived Humanness of AI Music
Imagery
Uptake
- 2023-07: ChatGPT: Early Adopters, Teething Issues and the Way Forward
- 2024-03: Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews
- 2024-05: Humlum, Anders and Vestergaard, Emilie, The Adoption of ChatGPT. IZA Discussion Paper No. 16992 doi: 10.2139/ssrn.4827166
- 2024-06: Kellogg, Katherine and Lifshitz-Assaf, Hila and Randazzo, Steven and Mollick, Ethan R. and Dell'Acqua, Fabrizio and McFowland III, Edward and Candelon, Francois and Lakhani, Karim R., Don't Expect Juniors to Teach Senior Professionals to Use Generative AI: Emerging Technology Risks and Novice AI Risk Mitigation Tactics (June 03, 2024). Harvard Business School Technology & Operations Mgt. Unit Working Paper 24-074, Harvard Business Working Paper No. 24-074, The Wharton School Research Paper doi: 10.2139/ssrn.4857373
- 2024-06: Delving into ChatGPT usage in academic writing through excess vocabulary
- 2024-09: The Rapid Adoption of Generative AI
- 2024-10: Growing Up: Navigating Generative AI’s Early Years – AI Adoption Report (executive summary, full report)
- 72% of leaders use genAI at least once a week (c.f. 23% in 2023); 90% agree AI enhances skills (c.f. 80% in 2023)
- Spending on genAI is up 130% (most companies plan to invest going forward)
- 2024-12: The unequal adoption of ChatGPT exacerbates existing inequalities among workers
- Higher adoption among young and less experienced
- Lower adoption among women and lower-earning workers
- 2025-02: The Widespread Adoption of Large Language Model-Assisted Writing Across Society: 10-25% adoption across a range of contexts
- 2025-02: Local Heterogeneity in Artificial Intelligence Jobs Over Time and Space
- 2025-04: Demand for LLMs: Descriptive Evidence on Substitution, Market Expansion, and Multihoming
- 2025-05: ChatGPT Is Still Leading the AI Wars but Google Gemini Is Gaining Ground
- 2025-05: Large Language Models, Small Labor Market Effects
- Significant uptake, but very little economic impact so far
- 2025-05: The Labor Market Effects of Generative Artificial Intelligence
- US worker usage of AI increasingly rapidly: 30% in 2024-12; 40% in 2025-05
- 2025-05: Trends – Artificial Intelligence
- 2025-06: Who is using AI to code? Global diffusion and impact of generative AI
- 2025-06: 2025 State of AI Report: The Builder’s Playbook A Practical Roadmap for AI Innovation
- 2025-07: METR: After the ChatGPT Moment: Measuring AI’s Adoption How quickly has AI been diffusing through the economy?
- 2025-07: Pew Research: 34% of U.S. adults have used ChatGPT, about double the share in 2023
- 2025-12: Epoch AI: Polling on AI Usage
Usage By
- 2026-02: The Politics of AI
Usage For
- 2024-12: Clio: A system for privacy-preserving insights into real-world AI use (Anthropic Clio)
- 2025-03: How People are Really Using Generative AI Now (writeup)
- 2025-04: Anthropic Education Report: How University Students Use Claude
- 2025-09: Anthropic Economic Index: Tracking AI's role in the US and global economy
- 2025-09: How People Use ChatGPT (OpenAI)
Hiding Usage
Societal Effects/Transformations
- 2024-09: Empirical evidence of Large Language Model's influence on human spoken communication
- 2025-09: Generative AI as Seniority-Biased Technological Change: Evidence from U.S. Résumé and Job Posting Data
Psychological Impact
- 2025-08: The Impact of Artificial Intelligence on Human Thought
- 2025-11: People readily follow personal advice from AI but it does not improve their well-being
Human Sentiment towards AI
- 2025-04: Pew Research: How the U.S. Public and AI Experts View Artificial Intelligence
- 2025-10: Pew Research: How People Around the World View AI: More are concerned than excited about its use, and more trust their own country and the EU to regulate it than trust the U.S. or China
- 2025-12: 2025 Edelman Trust Barometer
- 2025-12: Polling - Views of AI and data centers
- 2026-03: The Moralization of Artificial Intelligence
- 2026-03: Anthropic: What 81,000 people want from AI
AI Persuasion of Humans
(AI can update beliefs, change opinions, tackle conspiracy theories, etc.)
- 2022-11: Fine-tuning language models to find agreement among humans with diverse preferences
- 2024-08: Conversational AI Powered by Large Language Models Amplifies False Memories in Witness Interviews
- 2024-04: Just the facts: How dialogues with AI reduce conspiracy beliefs
- 2024-09: Durably reducing conspiracy beliefs through dialogues with AI
- 2025-03: Scaling language model size yields diminishing returns for single-message political persuasion
- 2025-04: Can AI Change Your View? Evidence from a Large-Scale Online Field Experiment
- 2025-05: On the conversational persuasiveness of GPT-4
- 2025-05: Large Language Models Are More Persuasive Than Incentivized Human Persuaders
- 2025-07: The Levers of Political Persuasion with Conversational AI
- 2025-12: Persuading voters using human–artificial intelligence dialogues
AI Effects on Human Psychology
Human well-being
- 2024-01: Loneliness and suicide mitigation for students using GPT3-enabled chatbots
- 2025-03: Investigating Affective Use and Emotional Well-being on ChatGPT
- 2025-03: How AI and Human Behaviors Shape Psychosocial Effects of Chatbot Use: A Longitudinal Controlled Study
- 2026-03: Practicing with Language Models Cultivates Human Empathic Communication
Counter loneliness
- 2023-11: Chatbots as social companions: How people perceive consciousness, human likeness, and social health benefits in machines
- 2024-07: AI Companions Reduce Loneliness
- 2025-03: How AI and Human Behaviors Shape Psychosocial Effects of Chatbot Use: A Longitudinal Controlled Study
- 2025-06: Anthropic: How People Use Claude for Support, Advice, and Companionship
Human mental abilities (creativity, learning)
- 2025-03: The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers
- 2025-06: Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task
Simulate Humans
- See also: Human brain
Sociology
- 2021-10: Explaining and predicting human behavior and social dynamics in simulated virtual worlds: reproducibility, generalizability, and robustness of causal discovery methods
- 2023-12: Google: Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordia
- 2024-02: Are Large Language Models (LLMs) Good Social Predictors?
- 2024-04: Automated Social Science: Language Models as Scientist and Subjects
- 2024-07: Perils and opportunities in using large language models in psychological research
- 2024-08: Predicting Results of Social Science Experiments Using Large Language Models
- 2024-10: Large Language Models based on historical text could offer informative tools for behavioral science
- 2025-04: LLM Social Simulations Are a Promising Research Method
- 2025-04: Measuring Human Leadership Skills with AI Agents
- 2025-04: SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users
- 2025-07: A foundation model to predict and capture human cognition (code)
- 2025-07: LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra
- 2025-09: General Social Agents
- 2025-12: Simulating Society Requires Simulating Thought
Theory of Mind
- 2025-08: How large language models encode theory-of-mind: a study on sparse parameter patterns
- 2025-10: Infusing Theory of Mind into Socially Intelligent LLM Agents
Humanlike Vibes
- 2025-07: The Xeno Sutra: Can Meaning and Value be Ascribed to an AI-Generated "Sacred" Text?
- 2025-10: LLMs Reproduce Human Purchase Intent via Semantic Similarity Elicitation of Likert Ratings
Skeptical
- 2025-08: Large Language Models Do Not Simulate Human Psychology
- 2026-02: Replicating Human Motivated Reasoning Studies with LLMs
