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| − | | + | #REDIRECT [[AI and Humans]] |
| − | =AI in Education=
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| − | ==Survey/study of==
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| − | * 2023-08: [https://www.nature.com/articles/s41598-023-38964-3 Perception, performance, and detectability of conversational artificial intelligence across 32 university courses]
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| − | * 2023-10: [https://www.bbc.com/worklife/article/20231017-the-employees-secretly-using-ai-at-work Employees] secretly using AI at work.
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| − | * 2023-10: [https://www.insidehighered.com/news/tech-innovation/artificial-intelligence/2023/10/31/most-students-outrunning-faculty-ai-use?utm_source=Inside+Higher+Ed&utm_campaign=23419446b9-DNU_2021_COPY_02&utm_medium=email&utm_term=0_1fcbc04421-23419446b9-236889242&mc_cid=23419446b9&mc_eid=dae49d931a Survey] shows students using AI more than professors.
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| − | * 2023-11: [https://www.nature.com/articles/d41586-023-03507-3 ChatGPT has entered the classroom: how LLMs could transform education]
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| − | | |
| − | ==AI improves learning/education==
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| − | * Mollick, Ethan R. and Mollick, Lilach and Bach, Natalie and Ciccarelli, LJ and Przystanski, Ben and Ravipinto, Daniel, [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4871171 AI Agents and Education: Simulated Practice at Scale] (June 17, 2024). The Wharton School Research Paper. [http://dx.doi.org/10.2139/ssrn.4871171 doi: 10.2139/ssrn.4871171]
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| − | ** Can enable personalized education.
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| − | * [https://arxiv.org/abs/2306.17156 Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutors]
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| − | ** GPT4 can out-perform human tutors.
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| − | * Keppler, Samantha and Sinchaisri, Wichinpong and Snyder, Clare, [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4924786 Backwards Planning with Generative AI: Case Study Evidence from US K12 Teachers] (August 13, 2024). [http://dx.doi.org/10.2139/ssrn.4924786 doi: 10.2139/ssrn.4924786]
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| − | ** Teachers benefit from using AI as a co-pilot to aid in tasks (planning, how to teach topic, explore ideas).
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| − | ** There is smaller utility in using AI purely as a text-generator (to make quizzes, workbooks, etc.).
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| − | * [https://arxiv.org/abs/2402.09809 Effective and Scalable Math Support: Evidence on the Impact of an AI- Tutor on Math Achievement in Ghana]
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| − | * [https://doi.org/10.21203/rs.3.rs-4243877/v1 AI Tutoring Outperforms Active Learning]
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| − | * [https://blogs.worldbank.org/en/education/From-chalkboards-to-chatbots-Transforming-learning-in-Nigeria From chalkboards to chatbots: Transforming learning in Nigeria, one prompt at a time]
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| − | ** 6 weeks of after-school AI tutoring = 2 years of typical learning gains
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| − | ** outperforms 80% of other educational interventions
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| − | | |
| − | ===AI can update beliefs===
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| − | * [https://www.science.org/doi/10.1126/science.adq1814 Durably reducing conspiracy beliefs through dialogues with AI]
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| − | * [https://osf.io/preprints/psyarxiv/h7n8u_v1 Just the facts: How dialogues with AI reduce conspiracy beliefs]
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| − | | |
| − | ==AI harms learning==
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| − | * [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0305354 A real-world test of artificial intelligence infiltration of a university examinations system: A “Turing Test” case study]
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| − | ** Current grading systems cannot detect AI.
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| − | * Bastani, Hamsa and Bastani, Osbert and Sungu, Alp and Ge, Haosen and Kabakcı, Özge and Mariman, Rei, [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4895486 Generative AI Can Harm Learning] (July 15, 2024). The Wharton School Research Paper.[http://dx.doi.org/10.2139/ssrn.4895486 doi: 10.2139/ssrn.4895486]
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| − | ** Access to ChatGPT harmed math education outcomes.
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| − | * 2024-09: [https://arxiv.org/abs/2409.09047 AI Meets the Classroom: When Does ChatGPT Harm Learning?]
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| − | | |
| − | ==Software/systems==
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| − | * [https://devpost.com/software/gptutor GPTutor] ([https://github.com/mynamegabe/GPTutor code])
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| − | * [https://arxiv.org/abs/2308.02773 EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education]
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| − | * [https://eurekalabs.ai/ Eureka Labs] (founded by [https://en.wikipedia.org/wiki/Andrej_Karpathy Andrej Karpathy]) aims to create AI-driven courses (first course is [https://github.com/karpathy/LLM101n Intro to LLMs])
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| − | | |
| − | ===LLMs===
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| − | * 2024-12: [https://www.arxiv.org/abs/2412.16429 LearnLM: Improving Gemini for Learning]
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| − | | |
| − | ===Individual tools===
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| − | * Chatbot (OpenAI [https://chatgpt.com/ ChatGPT], Anthropic [https://www.anthropic.com/claude Claude], Google [https://gemini.google.com/app Gemini])
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| − | * [https://notebooklm.google.com/ NotebookLM]: Enables one to "chat with documents".
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| − | * Google [https://learning.google.com/experiments/learn-about/signup Learn About]
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| − | | |
| − | ==AI for grading==
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| − | * [https://dl.acm.org/doi/10.1145/3657604.3664693 Can Large Language Models Make the Grade? An Empirical Study Evaluating LLMs Ability To Mark Short Answer Questions in K-12 Education] ([https://arxiv.org/abs/2405.02985 preprint])
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| − | ==Detection==
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| − | * [https://www.sciencedirect.com/science/article/pii/S2666920X24000109 Do teachers spot AI? Evaluating the detectability of AI-generated texts among student essays]
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| − | ** GenAI can simulate student writing in a way that teachers cannot detect.
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| − | ** AI essays are assessed more positively than student-written.
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| − | ** Teachers are overconfident in their source identification.
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| − | ** Both novice and experienced teachers could not identify texts generated by ChatGPT vs. students
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| − | ===AI Text Detectors Don't Work===
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| − | * 2024-05: [https://arxiv.org/abs/2405.07940 RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors]
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| − | * 2024-06: [https://arxiv.org/abs/2306.15666 Testing of Detection Tools for AI-Generated Text]
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| − | =AI/human=
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| − | ==AI out-performs humans==
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| − | ===Tests===
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| − | * 2023-07: [https://arxiv.org/abs/2307.10635 SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models]
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| − | * 2024-06: [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0305354 A real-world test of artificial intelligence infiltration of a university examinations system: A “Turing Test” case study]
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| − | ** AI scores higher than median students.
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| − | | |
| − | ===Creativity===
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| − | * 2023-07: [https://mackinstitute.wharton.upenn.edu/wp-content/uploads/2023/08/LLM-Ideas-Working-Paper.pdf Ideas Are Dimes A Dozen: Large Language Models For Idea Generation In Innovation]
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| − | * 2023-09: [https://www.nature.com/articles/s41598-023-40858-3 Best humans still outperform artificial intelligence in a creative divergent thinking task]
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| − | ** Best humans out-perform AI at creativity. (By implication, median humans may not.)
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| − | * 2024-02: [https://www.nature.com/articles/s41598-024-53303-w The current state of artificial intelligence generative language models is more creative than humans on divergent thinking tasks]
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| − | * 2024-02: Felin, Teppo and Holweg, Matthias, [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4737265 Theory Is All You Need: AI, Human Cognition, and Causal Reasoning] (February 24, 2024). [http://dx.doi.org/10.2139/ssrn.4737265 doi: 10.2139/ssrn.4737265]
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| − | ** Argues that human "theory-based" creativity is better than AI "data-based".
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| − | * 2024-07: [https://arxiv.org/abs/2407.01119 Pron vs Prompt: Can Large Language Models already Challenge a World-Class Fiction Author at Creative Text Writing?]
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| − | ** Top human (professional author) out-performs GPT4.
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| − | * 2024-09: [https://arxiv.org/abs/2409.04109 Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers]
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| − | ** LLMs can be creative
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| − | * 2024-09: [https://docs.iza.org/dp17302.pdf Creative and Strategic Capabilities of Generative AI: Evidence from Large-Scale Experiments]
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| − | | |
| − | ===Art===
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| − | * 2024-11: [https://doi.org/10.1038/s41598-024-76900-1 AI-generated poetry is indistinguishable from human-written poetry and is rated more favorably]
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| − | * 2024-11: [https://www.astralcodexten.com/p/how-did-you-do-on-the-ai-art-turing How Did You Do On The AI Art Turing Test?]
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| − | | |
| − | ===Professions===
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| − | * [https://agi.safe.ai/submit Humanity's Last Exam]
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| − | ** [https://x.com/alexandr_wang/status/1835738937719140440 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.
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| − | | |
| − | ====Medical====
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| − | * 2024-03: [https://www.medrxiv.org/content/10.1101/2024.03.12.24303785v1 Influence of a Large Language Model on Diagnostic Reasoning: A Randomized Clinical Vignette Study]
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| − | ** GPT4 improves medical practitioner work; surprisingly, GPT4 alone scored better than a human with GPT4 as aid (on selected tasks).
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| − | * 2024-10: [https://doi.org/10.1001/jamanetworkopen.2024.38535 Perspectives on Artificial Intelligence–Generated Responses to Patient Messages]
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| − | * 2024-10: [https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2825395 Large Language Model Influence on Diagnostic Reasoning; A Randomized Clinical Trial]
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| − | ** Use of ChatGPT does not strongly improve medical expert work; but AI alone out-scores human or human+AI
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| − | * 2024-11: [https://www.nature.com/articles/s41562-024-02046-9 Large language models surpass human experts in predicting neuroscience results] (writeup: [https://medicalxpress.com/news/2024-11-ai-neuroscience-results-human-experts.html AI can predict neuroscience study results better than human experts, study finds])
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| − | * 2024-12: [https://www.arxiv.org/abs/2412.10849 Superhuman performance of a large language model on the reasoning tasks of a physician]
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| − | * 2024-12: [https://arxiv.org/abs/2412.18925 HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs]
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| − | * 2025-02: [https://www.nature.com/articles/s41591-024-03456-y GPT-4 assistance for improvement of physician performance on patient care tasks: a randomized controlled trial]
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| − | * 2025-02: [https://www.nature.com/articles/s41591-025-03517-w Artificial intelligence for individualized treatment of persistent atrial fibrillation: a randomized controlled trial]
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| − | | |
| − | ====Therapy====
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| − | * 2025-02: [https://journals.plos.org/mentalhealth/article?id=10.1371/journal.pmen.0000145 When ELIZA meets therapists: A Turing test for the heart and mind]
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| − | | |
| − | ====Financial====
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| − | * 2024-07: [https://arxiv.org/abs/2407.17866 Financial Statement Analysis with Large Language Models]
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| − | ==AI improves human work==
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| − | * 2023-07: [https://www.science.org/doi/10.1126/science.adh2586 Experimental evidence on the productivity effects of generative artificial intelligence]
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| − | * 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., [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4573321 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 [http://dx.doi.org/10.2139/ssrn.4573321 doi: 10.2139/ssrn.4573321]
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| − | * 2023-11: [https://www.nber.org/papers/w31161 Generative AI at Work] (National Bureau of Economic Research)
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| − | * 2023-12: [https://osf.io/hdjpk The Uneven Impact of Generative AI on Entrepreneurial Performance] ([https://doi.org/10.31219/osf.io/hdjpk doi: 10.31219/osf.io/hdjpk])
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| − | * 2023-12: [https://arxiv.org/abs/2312.05481 Artificial Intelligence in the Knowledge Economy]: Non-autonomous AI (chatbot) benefits least knowledgeable workers; autonomous agents benefit the most knowledgeable workers
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| − | * 2024-07: [https://www.microsoft.com/en-us/research/publication/generative-ai-in-real-world-workplaces/ Generative AI in Real-World Workplaces: The Second Microsoft Report on AI and Productivity Research]
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| − | | |
| − | ===Coding===
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| − | * 2023-02: [https://arxiv.org/abs/2302.06590 The Impact of AI on Developer Productivity: Evidence from GitHub Copilot]
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| − | * 2024-09: Cui, Zheyuan and Demirer, Mert and Jaffe, Sonia and Musolff, Leon and Peng, Sida and Salz, Tobias, [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4945566 The Effects of Generative AI on High Skilled Work: Evidence from Three Field Experiments with Software Developers] (September 03, 2024). [http://dx.doi.org/10.2139/ssrn.4945566 doi: 10.2139/ssrn.4945566 ]
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| − | * 2024-11: Hoffmann, Manuel and Boysel, Sam and Nagle, Frank and Peng, Sida and Xu, Kevin, [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5007084 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, [http://dx.doi.org/10.2139/ssrn.5007084 doi: 10.2139/ssrn.5007084]
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| − | ===Forecasting===
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| − | * 2024-02: [https://arxiv.org/abs/2402.07862 AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracy]
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| − | ===Finance===
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| − | * 2024-12: [https://dx.doi.org/10.2139/ssrn.5075727 AI, Investment Decisions, and Inequality]: Novices see improvements in investment performance, sophisticated investors see even greater improvements.
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| − | | |
| − | ===Translation===
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| − | * 2025-01: [https://simonwillison.net/2025/Feb/2/workflow-for-translation/ A professional workflow for translation using LLMs] ([https://news.ycombinator.com/item?id=42897856 based on this])
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| − | ===Creativity===
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| − | * 2024-07: [https://www.science.org/doi/10.1126/sciadv.adn5290 Generative AI enhances individual creativity but reduces the collective diversity of novel content]
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| − | * 2024-08: [https://www.nature.com/articles/s41562-024-01953-1 An empirical investigation of the impact of ChatGPT on creativity]
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| − | * 2024-10: [https://arxiv.org/abs/2410.03703 Human Creativity in the Age of LLMs]
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| − | * 2024-11: [https://conference.nber.org/conf_papers/f210475.pdf Artificial Intelligence, Scientific Discovery, and Product Innovation]: diffusion model increases "innovation" (patents), boosts the best performers, but also removes some enjoyable tasks.
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| − | * 2024-12: [https://doi.org/10.1080/10400419.2024.2440691 Using AI to Generate Visual Art: Do Individual Differences in Creativity Predict AI-Assisted Art Quality?] ([https://osf.io/preprints/psyarxiv/ygzw6 preprint]): shows that more creative humans produce more creative genAI outputs
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| − | ===Equity===
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| − | * 2025-01: [https://ai.nejm.org/doi/full/10.1056/AIp2400889 Using Large Language Models to Promote Health Equity]
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| − | | |
| − | ===Counter loneliness===
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| − | * 2024-07: [https://arxiv.org/abs/2407.19096 AI Companions Reduce Loneliness]
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| − | ==Human Perceptions of AI==
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| − | * 2023-09: [https://www.nature.com/articles/d41586-023-02980-0 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.]
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| − | * 2024-11: [https://doi.org/10.1016/S2589-7500(24)00202-4 Attitudes and perceptions of medical researchers towards the use of artificial intelligence chatbots in the scientific process: an international cross-sectional survey] (Nature commentary: [https://www.nature.com/articles/s41592-024-02369-5 Quest for AI literacy])
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| − | ===AI passes Turing Test===
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| − | '''Text Dialog'''
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| − | * 2023-05: [https://arxiv.org/abs/2305.20010 Human or Not? A Gamified Approach to the Turing Test]
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| − | * 2023-10: [https://arxiv.org/abs/2310.20216 Does GPT-4 pass the Turing test?]
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| − | * 2024-05: [https://arxiv.org/abs/2405.08007 People cannot distinguish GPT-4 from a human in a Turing test]
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| − | * 2024-07: [https://arxiv.org/abs/2407.08853 GPT-4 is judged more human than humans in displaced and inverted Turing tests]
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| − | '''Art'''
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| − | * 2024-11: [https://www.astralcodexten.com/p/how-did-you-do-on-the-ai-art-turing 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.
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| − | * 2024-11: [https://doi.org/10.1038/s41598-024-76900-1 AI-generated poetry is indistinguishable from human-written poetry and is rated more favorably]
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| − | =Uptake=
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| − | * 2023-07: [https://doi.org/10.9734/ajrcos/2023/v16i4392 ChatGPT: Early Adopters, Teething Issues and the Way Forward]
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| − | * 2024-03: [https://arxiv.org/abs/2403.07183 Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews]
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| − | * 2024-05: Humlum, Anders and Vestergaard, Emilie, [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4827166 The Adoption of ChatGPT]. IZA Discussion Paper No. 16992 [http://dx.doi.org/10.2139/ssrn.4827166 doi: 10.2139/ssrn.4827166]
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| − | * 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., [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4857373 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 [http://dx.doi.org/10.2139/ssrn.4857373 doi: 10.2139/ssrn.4857373 ]
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| − | * 2024-06: [https://arxiv.org/abs/2406.07016 Delving into ChatGPT usage in academic writing through excess vocabulary]
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| − | * 2024-09: [https://static1.squarespace.com/static/60832ecef615231cedd30911/t/66f0c3fbabdc0a173e1e697e/1727054844024/BBD_GenAI_NBER_Sept2024.pdf The Rapid Adoption of Generative AI]
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| − | * 2024-10: [https://ai.wharton.upenn.edu/focus-areas/human-technology-interaction/2024-ai-adoption-report/ Growing Up: Navigating Generative AI’s Early Years – AI Adoption Report] ([https://ai.wharton.upenn.edu/wp-content/uploads/2024/10/AI-Report_Executive-Summary.pdf executive summary], [https://ai.wharton.upenn.edu/wp-content/uploads/2024/10/AI-Report_Full-Report.pdf full report])
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| − | ** 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)
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| − | ** Spending on genAI is up 130% (most companies plan to invest going forward)
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| − | * 2025-02: [https://arxiv.org/abs/2502.09747 The Widespread Adoption of Large Language Model-Assisted Writing Across Society]: 10-25% adoption across a range of contexts
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| − | ==Usage For==
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| − | * 2024-12: [https://assets.anthropic.com/m/7e1ab885d1b24176/original/Clio-Privacy-Preserving-Insights-into-Real-World-AI-Use.pdf Clio: A system for privacy-preserving insights into real-world AI use] (Anthropic [https://www.anthropic.com/research/clio Clio])
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| − | | |
| − | =See Also=
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| − | * [https://www.google.com/books/edition/_/cKnYEAAAQBAJ?hl=en&gbpv=1&pg=PA2 UNESCO. Guidance for Generative AI in Education and Research]
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| − | * [[AI]]
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