Experimental Evidence on Negative Impact of Generative AI on Scientific Learning Outcomes

preprint OA: closed
Full text JSON View at publisher
AI-generated summary by claude@2026-07, 2026-07-17

Complete reliance on generative AI for writing tasks reduced participant accuracy by 25.1%, while AI-assisted reading declined by 12%, though AI summarization improved outcomes.

One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works

AI-generated deep summary by claude@2026-07, 2026-07-17 · read from full text

The study experimentally tested how generative AI affects learning outcomes from academic reading materials by recruiting 32 college-educated participants who completed three cycles of reading, writing, and answering comprehension questions after receiving access to GPT-3.5 or GPT-4.0. After adjusting for background knowledge and demographics, participants who were fully reliant on AI for writing showed a 25.1% reduction in accuracy, and even AI-assisted reading produced a 12% decline, while using AI for summarization improved both summary quality and output; accuracy in the AI-assisted conditions also varied substantially. The author notes this pilot research is limited by its small sample size, preprint status (not peer reviewed), and the constrained task design (three selected passages and a short timed format) with reliance on self-selected AI familiarity criteria. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

Abstract In this study, I explored the impact of Generative AI on learning efficacy in academic reading materials using experimental methods. College-educated participants engaged in three cycles of reading and writing tasks. After each cycle, they responded to comprehension questions related to the material. After adjusting for background knowledge and demographic factors, complete reliance on AI for writing tasks led to a 25.1% reduction in accuracy. In contrast, AI-assisted reading resulted in a 12% decline. Interestingly, using AI for summarization significantly improved both quality and output. Accuracy exhibited notable variance in the AI-assisted section. Further analysis revealed that individuals with a robust background in the reading topic and superior reading/writing skills benefitted the most. I conclude the research by discussing educational policy implications, emphasizing the need for educators to warn students about the dangers of over-dependence on AI and provide guidance on its optimal use in educational settings.
Full text 497,365 characters · extracted from preprint-html · click to expand
Experimental Evidence on Negative Impact of Generative AI on Scientific Learning Outcomes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Experimental Evidence on Negative Impact of Generative AI on Scientific Learning Outcomes Qirui Ju This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3371292/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In this study, I explored the impact of Generative AI on learning efficacy in academic reading materials using experimental methods. College-educated participants engaged in three cycles of reading and writing tasks. After each cycle, they responded to comprehension questions related to the material. After adjusting for background knowledge and demographic factors, complete reliance on AI for writing tasks led to a 25.1% reduction in accuracy. In contrast, AI-assisted reading resulted in a 12% decline. Interestingly, using AI for summarization significantly improved both quality and output. Accuracy exhibited notable variance in the AI-assisted section. Further analysis revealed that individuals with a robust background in the reading topic and superior reading/writing skills benefitted the most. I conclude the research by discussing educational policy implications, emphasizing the need for educators to warn students about the dangers of over-dependence on AI and provide guidance on its optimal use in educational settings. Behavioral Economics Artificial Intelligence and Machine Learning Social Policy AI ChatGPT Education Productivity Learning Behavior Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Recent advancements in Generative Artificial Intelligence (AI) have ignited extensive discussions among scholars. Historically, Generative AI was primarily employed to produce text, code, and images from textual prompts. Notable applications of this technology include ChatGPT, designed for text generation, and Dall-E, tailored for image creation. Prior research, such as the study by Noy and Zhang (2023), has highlighted the positive implications of generative technology in the professional realm, emphasizing enhanced productivity due to the synergy between humans and machines. The educational sector is also affected by AI's evolution. Surveys conducted among educators and students have mostly revealed positive sentiments toward this burgeoning technology (Ali, 2023). Research by Kasneci et al. (2023) delves into the potential integration of AI within educational frameworks to foster personalized learning experiences. Furthermore, Kohnke et al. ( 2023 ) underscore the prospective benefits of tools like ChatGPT in facilitating second language acquisition. However, there are reservations. Notably, Fidelindo (2023) has expressed apprehensions regarding AI's role in nursing education, while Chukwuere ( 2023 ) has raised issues about potential breaches of privacy and academic integrity. Such divergent views on AI in education prompt several questions: Can AI enhance educational outcomes? Could guided chatbots, when integrated, amplify learning efficacy? And to what extent can AI assist in writing tasks? The objective of this research paper is to address these concerns, drawing on previous studies, and to experimentally examine the impact of AI. With participants having access to AI tools in the study, I aim to gauge the performance of college-educated individuals across the dimensions. Method This pilot paper serves as a pioneer study aimed at analyzing the impact of AI on learning effectiveness. The research took place from August 20th to 29th, 2023, incorporating widely recognized Generative AI tools such as GPT-3.5 and GPT-4.0, contingent upon the subscription tier. A total of 32 college-educated individuals were recruited from the online platform Prolific, evenly split with 16 males and 16 females. The participant demographics are detailed in Table 1 . Each participant underwent three rounds of tasks which entailed reading a paper, crafting a summary based on a given prompt, and answering five questions pertinent to the paper. These participants represented a wide spectrum of academic backgrounds, ranging from humanities and social sciences to natural sciences, medicine, and engineering. Prior to the study, participants were screened to verify their access to and understanding of Generative AI tools. They were presented with a question concerning their familiarity with AI tools, accompanied by a ChatGPT hyperlink as a reference. Only those who responded affirmatively proceeded further. Additionally, I evaluated their computer literacy, mathematical proficiency, highest educational attainment, and primary language to identify potential variables affecting their learning results. To gauge their computer aptitude, I inquired about their most advanced computer tasks, spanning non-code design, Office Suite proficiency, math/statistical coding, industry-level software development, and hardware/software research. Regarding their math expertise, questions covered topics from basic algebra and geometry to advanced subjects like dynamics, complexity, and algorithms. To ensure close attention and motivation in this experiment, participants were incentivized with a 30% bonus of payment if they answered 80% of the questions accurately. For each segment of the experiment, participants were given a maximum of 10 minutes to read the article and complete the writing task. The writing tasks fell into three categories: 1) Manual writing of summary reports without AI support (Manual), 2) Using AI for assistance in writing the summary report (AI), and 3) Individualized guided active reading through chat (Active). In the third category, participants weren't mandated to write a summary but could opt to use AI to enhance their comprehension by asking questions. For the manual writing task, participants were prohibited from copying and pasting from the reading material and writing boxes, ensuring they didn't utilize AI. I selected and adapted three passages from journal articles and the Graduate Record Examinations (GRE) for this experiment. The subjects spanned humanities, medicine, and engineering. The Flesch Reading Ease Score, which measures the complexity of papers, ranged between 25.5 to 34.4, indicating college and postgraduate-level complexity. The lengths of the papers are 631, 1347, and 1043 words respectively. (Table 2 ) To determine treatment and control groups, the three prompts were paired with the three passages, creating three distinct groups of three. Participants were then assigned to one of these groups. After reading each paper, participants had five minutes to answer five multiple-choice questions about the content. The order of paper within each group is randomized, ensuring the effect of tiredness does not impact final outcomes. While the quality of the summary output didn't impact the payment, the writing was evaluated on three criteria: comprehensiveness, grammar, and flow, each scored from 1 to 5. The overall grade was determined by averaging scores across these dimensions. Table 1 Demographic Information for Participants Column Compositions (%) Race White: 44.44% Black: 37.78% American Native: 13.33% Asian: 4.44% Highest Degree Bachelor: 66.67% Master: 22.22% Doctoral (Including JD or MD): 8.89% Associate: 2.22% Most Advanced Computer Task Statistics/Math Coding: 33.33% Office Suite: 31.11% Non-code Analysis/Design: 17.78% Hardware/Software Research: 13.33% Industrial-Level Development (GUI): 4.44% Most Advanced Math Class Calculus: 37.78% Algebra and Geometry: 37.78% Dynamics/Complexity Theory/Algorithms: 13.33% Linear Algebra/Probability: 6.67% Proof-based Class: 4.44% English Native No: 53.33% Yes: 46.67% Age Average: 32.41 Sex Female: 50.00% Male: 50.00% Student status No: 52.94% Yes: 47.06% Employment status Full-Time: 55.88% Unemployed (and job seeking): 20.59% Part-Time: 14.71% Due to start a new job within the next month: 5.88% Other: 2.94% Table 2 Summary Statistics for Performance Metric Count Mean Std Deviation Min 25th Percentile Median 75th Percentile Max Flesch_Reading_Ease Score 135 30.67 3.79 25.5 25.5 32.1 34.4 34.4 Multiple Choice Time 54 175.72 81.86 21 100 178.5 253 302 Summary Report Time 54 554.06 108.52 181 594 601 601 603 Correctness (%) 135 0.547 0.301 0 0.4 0.6 0.8 1 Length of Summary Report 135 963.19 1393.89 3 146 378 1171.5 10525 Content Grade 135 3.27 1.6 1 1 4 5 5 Flow Grade 135 3.73 1.6 1 3 5 5 5 Grammar Grade 135 3.19 1.42 1 2.5 3 4.5 5 Overall Grade 135 3.39 1.41 1 3 4 4 5 Results Accuracy In the study, participants who tackled writing assignments without any external assistance achieved an average grade of 66.7%. Conversely, those fully reliant on AI for crafting their assignments scored an average of 48.4%. The "Active" group, which utilized AI to aid their comprehension of the paper, secured an average of 48.9% and exhibited the most diverse grade distribution, as showcased in Fig. 1 . The observed differences are statistically significant at the 5% level, with p-values of 0.002459 between the AI and Manual groups, and 0.005081 between the Active and Manual groups. The distribution pattern for the AI-assisted results aligns closely with a normal distribution. However, the Active group's distribution is bimodal, indicating that the benefits of using AI to enhance paper comprehension vary among participants. The standard deviations for the Active, AI, and Manual groups are 0.3178209, 0.287588, and 0.266287, respectively. Table 3 Correctness Regression To prepare the data for regression analysis, I encoded both the background knowledge and demographic details into distinct dummy variables. This encoding helps determine the factors influencing accuracy. In this study, "simple coding" encompasses tasks related to the Office Suite and non-code design, whereas "advanced computer tasks" include math/statistical coding, industry-standard design, and software/hardware research. For mathematical proficiency, categories are delineated as "advanced math" (covering Dynamics, Complexity Theory, Algorithms, and Proof-based Classes) and "basic college-level math" (comprising Algebra and Geometry, Linear Algebra/Probability, and Calculus). Then I constructed the regression equation below: $$\varvec{Y}=\varvec{\alpha }+ \varvec{\beta }\varvec{X}+\varvec{e}$$ \({\beta }\) includes: whether the topic of the paper matches with the best field of a participant (dummy), age, overall grade from the manual writing section, math class dummies (advanced math class vs. basic math class), coding skills dummies (advanced vs. simple), native speaker dummy, reading material difficulty level, and degree dummy. X is the independent variable; Y is the correctness for each paper. \(\varvec{\alpha }\) is the intercept, and e represents residuals. According to the result in Table 3 , when controlling the background knowledge, difficulty of reading materials, and other demographic information, when entirely relying on AI, the accuracy of learning outcomes dropped by 25.1% and when partly relying on AI, the accuracy dropped by 12%. Both drops are statistically significant. Productivity and Quality of Writing From the experiment, participants in the AI group showed a boost in both quality and productivity. Compared to the manual group, participants in both Active and AI groups spent less time. Nearly all participants in the manual section spent all their time on writing, while a significant number of participants in the Active and AI groups submitted their works in advance, lowering the average completion rate to 9 minutes for Active and 8.7 minutes for AI. (Fig. 3 ) As described in the previous section, the quality of writing in this experiment was assessed based on three dimensions on a scale of 1 to 5: flow, grammar, and coverage. Compared to the manual group, the AI group generally produces higher-quality writing in the given time. (Fig. 4 ) Writings by AI often have fewer spelling errors, better flow, and coverage compared to human writings. This result suggests a complementary relationship between humans and machines, which is consistent with prior studies. Factors for Affecting Individualized Learning Outcomes In the previous section, the correctness rate of the active group demonstrated a bipolar distribution, suggesting the effect of individualized active reading strategy may generate different impacts on different groups of people. (Table 4 ) Table 4. Regression on Correctness Boost in Active Section When participants were trying to learn a topic that was not their best field, their accuracy lowered by 0.109, which is significant at 0.05 level. Both age and the writing score in the manual section have a positive significant impact on the difference of correctness at 0.008 and 0.154. Coding skill does not appear to be a significant source of correctness change. Master’s degree holders in this experiment typically perform better than associate degree holders. Speaking English as a native language has a positive significant impact on the correctness level. Discussion Human-Machine Substitution and Risk of Misinformation From the inception of Generative AI, scholars have ardently explored its impact on productivity. A considerable number of these studies have heralded the technology as a significant booster of efficiency. For instance, Yang (2023) detailed the favorable effects of ChatGPT on Taiwanese companies. Similarly, Czarnezki et al. (2023) underscored the enhanced productivity that AI brings, based on firm-level data. This experiment, focusing on writing tasks, is consistent with these findings, highlighting AI's positive influence on writing performance. However, the distinction between the academic realm and the professional sphere extends beyond mere productivity gains. Often, the ultimate objective in education is cognitive enhancement or knowledge expansion, with the summary report serving as a tool to achieve this aim. Historically, students in higher education have been required to meticulously read and understand academic papers before crafting a summary (manual). Yet, with AI's assistance, students might be tempted to sidestep this in-depth exploration. An increasing reliance on AI tools in the educational sector poses significant challenges, especially when it comes to the authenticity and accuracy of information. AI-driven systems, while advanced, are not infallible. They can, at times, provide plausible but inaccurate information based on their training data or algorithms. (Zhuo et al., 2023 ; Bian et al., 2023 ) This risk becomes even more pronounced when students don't deeply engage with their reading material. If students merely skim content or rely solely on AI-generated summaries, they might not develop the critical skills needed to discern fact from fiction. They would be accepting AI outputs at face value without the foundational knowledge to challenge or verify the information. In essence, without mastering the knowledge through rigorous study and engagement, students become more susceptible to accepting and propagating false information. Viewed in this light, AI appears to replace human involvement in the paper-writing process, which could potentially detract from the intended cognitive outcomes and risk misinformation. Unequal Benefit from Customized Learning Experience and Education Policy From the findings in this paper, introducing ChatGPT for a tailored learning experience may paradoxically reduce overall correctness. However, this approach did yield the highest standard deviation among Active groups. Notably, individuals who displayed pronounced proficiency in the manual writing section often possessed a deep understanding of the subject matter of the passages. Being native English speakers and holding master's degrees, they had the advantage of grasping linguistic nuances. When given the choice to use AI to help understand the reading material, they generally achieve the largest correctness increase by using their prior knowledge and reading skills. While AI offers a revolutionary avenue to customize learning experiences, it's evident that technology on its own doesn't ensure enhanced learning outcomes. Without external guidance, individuals may find it challenging to leverage AI tools effectively. As Tobias ( 1994 ) and Dochy (1994) found, interest, prior knowledge, and the increase of prior knowledge play significant roles in learning efficiency. Their results resonate with the observations made in this study. When introduced into the education schema, guidance from instructors could often help students with little prior knowledge better utilize AI chatbots to comprehend their reading material, thereby improving their overall grades. This change in the method of instruction could lead to a different role for instructors in the AI era. Limitations There are several limitations in this paper. First and foremost, the scope of this paper is predominantly centered on the higher education system. While the analysis and results offer valuable insights into this particular sector, they might not be directly applicable or reflective of the broader education spectrum. Elementary and secondary education systems have distinct characteristics, teaching methodologies, and challenges. The dynamics of young learners differ from those in tertiary education, and the integration of AI at these levels might have different implications. Thus, the results presented here may not be generalized to these other educational systems without further research. Secondly, the time frame of my experiment might not provide a comprehensive view of the AI's efficiency and effectiveness in the educational process. Participants were allocated a maximum of 39 minutes to complete all tasks, a relatively short duration. It's plausible that the benefits or drawbacks of AI assistance might manifest differently over longer periods. For instance, while AI might prove beneficial for quick tasks, its impact on long-term retention and understanding remains unknown based on this study. Moreover, learners might experience cognitive fatigue or increased reliance on AI over extended periods, factors not accounted for in this short duration. Lastly, the versatility and potential applications of AI in education are vast. This paper narrowly focuses on its role in assisting with writing tasks. While this provides a detailed insight into this specific area, it doesn't account for the myriad of other ways AI could revolutionize education. From coding assistance to design guidance, and from picture editing to complex problem-solving, AI has the potential to reshape various facets of educational instruction and practice. Also, because of different AI chatbots, other types of chatbots could provide different services, leading the final learning outcomes to become divergent. Each of these applications could have unique benefits, challenges, and implications for learners. By concentrating solely on writing assistance, this paper might not capture the full breadth of AI's impact on the educational domain. Conclusion In this paper, I examined the influence of Generative AI on learning effectiveness. The results indicated a decrease in learning effectiveness in both AI-based and AI-assisted scenarios, with declines of 25.1% and 12% respectively. Notably, the AI-assisted group exhibited the greatest variance among all groups. Upon evaluating factors that could contribute to enhanced efficiency with AI assistance, I determined that the efficacy of AI-assisted learning is significantly influenced by a student's prior knowledge. These findings have important policy implications: educators need to make students aware of the potential drawbacks of employing AI in their studies. Furthermore, if educators choose to incorporate AI into the curriculum, they must provide clear guidelines on its appropriate use. Declarations Funding Acknowledgement The author appreciates the support from Duke Innovation Co-lab. Competing Interest Declaration The author declares that he has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethics This pre-registered experiment follows the protocol at Duke Institutional Review Board. Data Availability All data and code are available on OSF. References Bian, N., Liu, P., Han, X., Lin, H., Lu, Y., He, B., & Sun, L. (2023). A Drop of Ink Makes a Million Think: The Spread of False Information in Large Language Models (arXiv:2305.04812). arXiv. https://doi.org/10.48550/arXiv.2305.04812 Chukwuere, J. E. (2023). ChatGPT: The game changer for higher education institutions. Jozac Academic Voice, 3(1), Article 1. Damioli, G., Van Roy, V., & Vertesy, D. (2021). The impact of artificial intelligence on labor productivity. Eurasian Business Review, 11(1), 1–25. https://doi.org/10.1007/s40821-020-00172-8 Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models (arXiv:2303.10130). arXiv. https://doi.org/10.48550/arXiv.2303.10130 Felten, E. W., Raj, M., & Seamans, R. (2023). How will Language Modelers like ChatGPT Affect Occupations and Industries? (SSRN Scholarly Paper 4375268). https://doi.org/10.2139/ssrn.4375268 Hailikari, T., Katajavuori, N., & Lindblom-Ylanne, S. (2008). The Relevance of Prior Knowledge in Learning and Instructional Design. American Journal of Pharmaceutical Education, 72(5), 113. Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274 Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for Language Teaching and Learning. RELC Journal, 54(2), 537–550. https://doi.org/10.1177/00336882231162868 Lim, F. (2023). Machine-Generated Writing and Chatbots: Nursing Education’s Fear of the Unknown. Nursing Education Perspectives, 44(4), 203. https://doi.org/10.1097/01.NEP.0000000000001147 McGee, R. (2023). Using Artificial Intelligence (AI) to Compose a Musical Score for a Taekwondo Tournament Routine: A ChatGPT Experiment. https://doi.org/10.13140/RG.2.2.11235.22569 Mitrović, S., Andreoletti, D., & Ayoub, O. (2023). ChatGPT or Human? Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPT-generated Text (arXiv:2301.13852). arXiv. https://doi.org/10.48550/arXiv.2301.13852 Noy, S., & Zhang, W. (2023a). Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence (SSRN Scholarly Paper 4375283). https://doi.org/10.2139/ssrn.4375283 Noy, S., & Zhang, W. (2023b). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh2586 Oguz, F. E., Ekersular, M. N., Sunnetci, K. M., & Alkan, A. (2023). Can Chat GPT be Utilized in Scientific and Undergraduate Studies? Annals of Biomedical Engineering. https://doi.org/10.1007/s10439-023-03333-8 Opara, E., Mfon-Ette Theresa, A., & Aduke, T. C. (2023). ChatGPT for Teaching, Learning and Research: Prospects and Challenges (SSRN Scholarly Paper 4375470). https://papers.ssrn.com/abstract=4375470 Pardos, Z. A., & Bhandari, S. (2023). Learning gain differences between ChatGPT and human tutor generated algebra hints (arXiv:2302.06871). arXiv. https://doi.org/10.48550/arXiv.2302.06871 Pieters, J. M., Breuer, K., & Simons, P. R.-J. (2012). Learning Environments: Contributions from Dutch and German Research. Springer Science & Business Media. Tobias, S. (1994). Interest, Prior Knowledge, and Learning. Review of Educational Research, 64(1), 37–54. https://doi.org/10.3102/00346543064001037 von Rueden, L., Mayer, S., Beckh, K., Georgiev, B., Giesselbach, S., Heese, R., Kirsch, B., Pfrommer, J., Pick, A., Ramamurthy, R., Walczak, M., Garcke, J., Bauckhage, C., & Schuecker, J. (2023). Informed Machine Learning – A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems. IEEE Transactions on Knowledge and Data Engineering, 35(1), 614–633. https://doi.org/10.1109/TKDE.2021.3079836 Yang, C.-H. (2022). How Artificial Intelligence Technology Affects Productivity and Employment: Firm-level Evidence from Taiwan. Research Policy, 51(6), 104536. https://doi.org/10.1016/j.respol.2022.104536 Zheng, S., Trott, A., Srinivasa, S., Naik, N., Gruesbeck, M., Parkes, D. C., & Socher, R. (2020). The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies (arXiv:2004.13332). arXiv. https://doi.org/10.48550/arXiv.2004.13332 Zhuo, T. Y., Huang, Y., Chen, C., & Xing, Z. (2023). Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity (arXiv:2301.12867). arXiv. https://doi.org/10.48550/arXiv.2301.12867 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3371292","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":234835212,"identity":"fe09d3f7-86d2-4e62-9379-3223800d1a1d","order_by":0,"name":"Qirui Ju","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYFCCg+0fPrAxGICYEkRqOdzGOINELextzDwkaeE7eLDtsU3ZYWN+BuaDt3mI0SJ54GC7cc65w2aSDWzJ1kRpMThwsEE6t+2wjcEBHjNp4rVYArXYH+D/RrSWNmnGtsNmBgw8bMRpAfql2bDnXLqxxGE2Y8s5xGjhu3H84YMfZdaG/e3ND2+8IUYLw40DUAYzUcpB4HwD0UpHwSgYBaNgpAIAxS41XuISgLUAAAAASUVORK5CYII=","orcid":"","institution":"Duke University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Qirui","middleName":"","lastName":"Ju","suffix":""}],"badges":[],"createdAt":"2023-09-20 04:02:34","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-3371292/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3371292/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":43484834,"identity":"9845c7ab-35e5-44e9-887f-fde4bffe9b39","added_by":"auto","created_at":"2023-09-21 14:09:42","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":165353,"visible":true,"origin":"","legend":"\u003cp\u003eCorrectness Across Different Treatment Groups\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3371292/v1/691659d9663cd3659a845068.jpeg"},{"id":43484837,"identity":"2c15dac5-8b42-42ac-a5a6-f122942b560e","added_by":"auto","created_at":"2023-09-21 14:09:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23168,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Correctness Across Different Groups\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3371292/v1/8e37d7a811a1495b1d724c41.png"},{"id":43484835,"identity":"86999593-65fc-462f-8cf5-e70d534b7f88","added_by":"auto","created_at":"2023-09-21 14:09:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":22214,"visible":true,"origin":"","legend":"\u003cp\u003eCorrectness Across Different Treatment Groups\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3371292/v1/822dbb4f7ac32d1982b4e830.png"},{"id":43486003,"identity":"338b72e2-d165-4e78-8124-d76969b7250b","added_by":"auto","created_at":"2023-09-21 14:17:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":37071,"visible":true,"origin":"","legend":"\u003cp\u003eScore Distribution\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3371292/v1/ed32725f492b187f0594413c.png"},{"id":43486361,"identity":"f158d570-8699-43fc-82df-43c5d5dde116","added_by":"auto","created_at":"2023-09-21 14:25:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":580433,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3371292/v1/25ea3cc5-24a8-44cc-b019-de5a36d14216.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eExperimental Evidence on Negative Impact of Generative AI on Scientific Learning Outcomes\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRecent advancements in Generative Artificial Intelligence (AI) have ignited extensive discussions among scholars. Historically, Generative AI was primarily employed to produce text, code, and images from textual prompts. Notable applications of this technology include ChatGPT, designed for text generation, and Dall-E, tailored for image creation. Prior research, such as the study by Noy and Zhang (2023), has highlighted the positive implications of generative technology in the professional realm, emphasizing enhanced productivity due to the synergy between humans and machines.\u003c/p\u003e \u003cp\u003eThe educational sector is also affected by AI's evolution. Surveys conducted among educators and students have mostly revealed positive sentiments toward this burgeoning technology (Ali, 2023). Research by Kasneci et al. (2023) delves into the potential integration of AI within educational frameworks to foster personalized learning experiences. Furthermore, Kohnke et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) underscore the prospective benefits of tools like ChatGPT in facilitating second language acquisition. However, there are reservations. Notably, Fidelindo (2023) has expressed apprehensions regarding AI's role in nursing education, while Chukwuere (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) has raised issues about potential breaches of privacy and academic integrity. Such divergent views on AI in education prompt several questions: Can AI enhance educational outcomes? Could guided chatbots, when integrated, amplify learning efficacy? And to what extent can AI assist in writing tasks?\u003c/p\u003e \u003cp\u003eThe objective of this research paper is to address these concerns, drawing on previous studies, and to experimentally examine the impact of AI. With participants having access to AI tools in the study, I aim to gauge the performance of college-educated individuals across the dimensions.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eThis pilot paper serves as a pioneer study aimed at analyzing the impact of AI on learning effectiveness. The research took place from August 20th to 29th, 2023, incorporating widely recognized Generative AI tools such as GPT-3.5 and GPT-4.0, contingent upon the subscription tier. A total of 32 college-educated individuals were recruited from the online platform Prolific, evenly split with 16 males and 16 females. The participant demographics are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Each participant underwent three rounds of tasks which entailed reading a paper, crafting a summary based on a given prompt, and answering five questions pertinent to the paper. These participants represented a wide spectrum of academic backgrounds, ranging from humanities and social sciences to natural sciences, medicine, and engineering. Prior to the study, participants were screened to verify their access to and understanding of Generative AI tools. They were presented with a question concerning their familiarity with AI tools, accompanied by a ChatGPT hyperlink as a reference. Only those who responded affirmatively proceeded further. Additionally, I evaluated their computer literacy, mathematical proficiency, highest educational attainment, and primary language to identify potential variables affecting their learning results. To gauge their computer aptitude, I inquired about their most advanced computer tasks, spanning non-code design, Office Suite proficiency, math/statistical coding, industry-level software development, and hardware/software research. Regarding their math expertise, questions covered topics from basic algebra and geometry to advanced subjects like dynamics, complexity, and algorithms.\u003c/p\u003e \u003cp\u003eTo ensure close attention and motivation in this experiment, participants were incentivized with a 30% bonus of payment if they answered 80% of the questions accurately. For each segment of the experiment, participants were given a maximum of 10 minutes to read the article and complete the writing task. The writing tasks fell into three categories: 1) Manual writing of summary reports without AI support (Manual), 2) Using AI for assistance in writing the summary report (AI), and 3) Individualized guided active reading through chat (Active). In the third category, participants weren't mandated to write a summary but could opt to use AI to enhance their comprehension by asking questions. For the manual writing task, participants were prohibited from copying and pasting from the reading material and writing boxes, ensuring they didn't utilize AI.\u003c/p\u003e \u003cp\u003eI selected and adapted three passages from journal articles and the Graduate Record Examinations (GRE) for this experiment. The subjects spanned humanities, medicine, and engineering. The Flesch Reading Ease Score, which measures the complexity of papers, ranged between 25.5 to 34.4, indicating college and postgraduate-level complexity. The lengths of the papers are 631, 1347, and 1043 words respectively. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) To determine treatment and control groups, the three prompts were paired with the three passages, creating three distinct groups of three. Participants were then assigned to one of these groups. After reading each paper, participants had five minutes to answer five multiple-choice questions about the content. The order of paper within each group is randomized, ensuring the effect of tiredness does not impact final outcomes.\u003c/p\u003e \u003cp\u003eWhile the quality of the summary output didn't impact the payment, the writing was evaluated on three criteria: comprehensiveness, grammar, and flow, each scored from 1 to 5. The overall grade was determined by averaging scores across these dimensions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Information for Participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColumn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompositions (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite: 44.44%\u003c/p\u003e \u003cp\u003eBlack: 37.78%\u003c/p\u003e \u003cp\u003eAmerican Native: 13.33%\u003c/p\u003e \u003cp\u003eAsian: 4.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor: 66.67%\u003c/p\u003e \u003cp\u003eMaster: 22.22%\u003c/p\u003e \u003cp\u003eDoctoral (Including JD or MD): 8.89%\u003c/p\u003e \u003cp\u003eAssociate: 2.22%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost Advanced Computer Task\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistics/Math Coding: 33.33%\u003c/p\u003e \u003cp\u003eOffice Suite: 31.11%\u003c/p\u003e \u003cp\u003eNon-code Analysis/Design: 17.78%\u003c/p\u003e \u003cp\u003eHardware/Software Research: 13.33%\u003c/p\u003e \u003cp\u003eIndustrial-Level Development (GUI): 4.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost Advanced Math Class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalculus: 37.78%\u003c/p\u003e \u003cp\u003eAlgebra and Geometry: 37.78%\u003c/p\u003e \u003cp\u003eDynamics/Complexity Theory/Algorithms: 13.33%\u003c/p\u003e \u003cp\u003eLinear Algebra/Probability: 6.67%\u003c/p\u003e \u003cp\u003eProof-based Class: 4.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnglish Native\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo: 53.33%\u003c/p\u003e \u003cp\u003eYes: 46.67%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage: 32.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale: 50.00%\u003c/p\u003e \u003cp\u003eMale: 50.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo: 52.94%\u003c/p\u003e \u003cp\u003eYes: 47.06%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFull-Time: 55.88%\u003c/p\u003e \u003cp\u003eUnemployed (and job seeking): 20.59%\u003c/p\u003e \u003cp\u003ePart-Time: 14.71%\u003c/p\u003e \u003cp\u003eDue to start a new job within the next month: 5.88%\u003c/p\u003e \u003cp\u003eOther: 2.94%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary Statistics for Performance\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25th Percentile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e75th Percentile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlesch_Reading_Ease Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e34.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple Choice Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e175.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e178.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummary Report Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e554.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrectness (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of Summary Report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e963.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1393.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1171.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContent Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlow Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrammar Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eAccuracy\u003c/h2\u003e\n \u003cp\u003eIn the study, participants who tackled writing assignments without any external assistance achieved an average grade of 66.7%. Conversely, those fully reliant on AI for crafting their assignments scored an average of 48.4%. The \u0026quot;Active\u0026quot; group, which utilized AI to aid their comprehension of the paper, secured an average of 48.9% and exhibited the most diverse grade distribution, as showcased in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The observed differences are statistically significant at the 5% level, with p-values of 0.002459 between the AI and Manual groups, and 0.005081 between the Active and Manual groups. The distribution pattern for the AI-assisted results aligns closely with a normal distribution. However, the Active group\u0026apos;s distribution is bimodal, indicating that the benefits of using AI to enhance paper comprehension vary among participants. The standard deviations for the Active, AI, and Manual groups are 0.3178209, 0.287588, and 0.266287, respectively.\u003c/p\u003e\n \u003cp\u003eTable 3 Correctness Regression\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"552\" height=\"938\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eTo prepare the data for regression analysis, I encoded both the background knowledge and demographic details into distinct dummy variables. This encoding helps determine the factors influencing accuracy. In this study, \u0026quot;simple coding\u0026quot; encompasses tasks related to the Office Suite and non-code design, whereas \u0026quot;advanced computer tasks\u0026quot; include math/statistical coding, industry-standard design, and software/hardware research. For mathematical proficiency, categories are delineated as \u0026quot;advanced math\u0026quot; (covering Dynamics, Complexity Theory, Algorithms, and Proof-based Classes) and \u0026quot;basic college-level math\u0026quot; (comprising Algebra and Geometry, Linear Algebra/Probability, and Calculus).\u003c/p\u003e\n \u003cp\u003eThen I constructed the regression equation below:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\varvec{Y}=\\varvec{\\alpha }+ \\varvec{\\beta }\\varvec{X}+\\varvec{e}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({\\beta }\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e includes: whether the topic of the paper matches with the best field of a participant (dummy), age, overall grade from the manual writing section, math class dummies (advanced math class vs. basic math class), coding skills dummies (advanced vs. simple), native speaker dummy, reading material difficulty level, and degree dummy. X is the independent variable; Y is the correctness for each paper. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{\\alpha }\\)\u003c/span\u003e\u003c/span\u003e is the intercept, and e represents residuals.\u003c/p\u003e\n \u003cp\u003eAccording to the result in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, when controlling the background knowledge, difficulty of reading materials, and other demographic information, when entirely relying on AI, the accuracy of learning outcomes dropped by 25.1% and when partly relying on AI, the accuracy dropped by 12%. Both drops are statistically significant.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eProductivity and Quality of Writing\u003c/h2\u003e\n \u003cp\u003eFrom the experiment, participants in the AI group showed a boost in both quality and productivity. Compared to the manual group, participants in both Active and AI groups spent less time. Nearly all participants in the manual section spent all their time on writing, while a significant number of participants in the Active and AI groups submitted their works in advance, lowering the average completion rate to 9 minutes for Active and 8.7 minutes for AI. (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eAs described in the previous section, the quality of writing in this experiment was assessed based on three dimensions on a scale of 1 to 5: flow, grammar, and coverage. Compared to the manual group, the AI group generally produces higher-quality writing in the given time. (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) Writings by AI often have fewer spelling errors, better flow, and coverage compared to human writings. This result suggests a complementary relationship between humans and machines, which is consistent with prior studies.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eFactors for Affecting Individualized Learning Outcomes\u003c/h2\u003e\n \u003cp\u003eIn the previous section, the correctness rate of the active group demonstrated a bipolar distribution, suggesting the effect of individualized active reading strategy may generate different impacts on different groups of people. (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eTable 4. Regression on Correctness Boost in Active Section\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"587\" height=\"691\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eWhen participants were trying to learn a topic that was not their best field, their accuracy lowered by 0.109, which is significant at 0.05 level. Both age and the writing score in the manual section have a positive significant impact on the difference of correctness at 0.008 and 0.154. Coding skill does not appear to be a significant source of correctness change. Master\u0026rsquo;s degree holders in this experiment typically perform better than associate degree holders. Speaking English as a native language has a positive significant impact on the correctness level.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eHuman-Machine Substitution and Risk of Misinformation\u003c/h2\u003e \u003cp\u003eFrom the inception of Generative AI, scholars have ardently explored its impact on productivity. A considerable number of these studies have heralded the technology as a significant booster of efficiency. For instance, Yang (2023) detailed the favorable effects of ChatGPT on Taiwanese companies. Similarly, Czarnezki et al. (2023) underscored the enhanced productivity that AI brings, based on firm-level data. This experiment, focusing on writing tasks, is consistent with these findings, highlighting AI's positive influence on writing performance.\u003c/p\u003e \u003cp\u003eHowever, the distinction between the academic realm and the professional sphere extends beyond mere productivity gains. Often, the ultimate objective in education is cognitive enhancement or knowledge expansion, with the summary report serving as a tool to achieve this aim. Historically, students in higher education have been required to meticulously read and understand academic papers before crafting a summary (manual). Yet, with AI's assistance, students might be tempted to sidestep this in-depth exploration.\u003c/p\u003e \u003cp\u003eAn increasing reliance on AI tools in the educational sector poses significant challenges, especially when it comes to the authenticity and accuracy of information. AI-driven systems, while advanced, are not infallible. They can, at times, provide plausible but inaccurate information based on their training data or algorithms. (Zhuo et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bian et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) This risk becomes even more pronounced when students don't deeply engage with their reading material. If students merely skim content or rely solely on AI-generated summaries, they might not develop the critical skills needed to discern fact from fiction. They would be accepting AI outputs at face value without the foundational knowledge to challenge or verify the information. In essence, without mastering the knowledge through rigorous study and engagement, students become more susceptible to accepting and propagating false information. Viewed in this light, AI appears to replace human involvement in the paper-writing process, which could potentially detract from the intended cognitive outcomes and risk misinformation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eUnequal Benefit from Customized Learning Experience and Education Policy\u003c/h3\u003e\n\u003cp\u003eFrom the findings in this paper, introducing ChatGPT for a tailored learning experience may paradoxically reduce overall correctness. However, this approach did yield the highest standard deviation among Active groups. Notably, individuals who displayed pronounced proficiency in the manual writing section often possessed a deep understanding of the subject matter of the passages. Being native English speakers and holding master's degrees, they had the advantage of grasping linguistic nuances. When given the choice to use AI to help understand the reading material, they generally achieve the largest correctness increase by using their prior knowledge and reading skills.\u003c/p\u003e \u003cp\u003eWhile AI offers a revolutionary avenue to customize learning experiences, it's evident that technology on its own doesn't ensure enhanced learning outcomes. Without external guidance, individuals may find it challenging to leverage AI tools effectively. As Tobias (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) and Dochy (1994) found, interest, prior knowledge, and the increase of prior knowledge play significant roles in learning efficiency. Their results resonate with the observations made in this study. When introduced into the education schema, guidance from instructors could often help students with little prior knowledge better utilize AI chatbots to comprehend their reading material, thereby improving their overall grades. This change in the method of instruction could lead to a different role for instructors in the AI era.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eThere are several limitations in this paper. First and foremost, the scope of this paper is predominantly centered on the higher education system. While the analysis and results offer valuable insights into this particular sector, they might not be directly applicable or reflective of the broader education spectrum. Elementary and secondary education systems have distinct characteristics, teaching methodologies, and challenges. The dynamics of young learners differ from those in tertiary education, and the integration of AI at these levels might have different implications. Thus, the results presented here may not be generalized to these other educational systems without further research.\u003c/p\u003e \u003cp\u003eSecondly, the time frame of my experiment might not provide a comprehensive view of the AI's efficiency and effectiveness in the educational process. Participants were allocated a maximum of 39 minutes to complete all tasks, a relatively short duration. It's plausible that the benefits or drawbacks of AI assistance might manifest differently over longer periods. For instance, while AI might prove beneficial for quick tasks, its impact on long-term retention and understanding remains unknown based on this study. Moreover, learners might experience cognitive fatigue or increased reliance on AI over extended periods, factors not accounted for in this short duration.\u003c/p\u003e \u003cp\u003eLastly, the versatility and potential applications of AI in education are vast. This paper narrowly focuses on its role in assisting with writing tasks. While this provides a detailed insight into this specific area, it doesn't account for the myriad of other ways AI could revolutionize education. From coding assistance to design guidance, and from picture editing to complex problem-solving, AI has the potential to reshape various facets of educational instruction and practice. Also, because of different AI chatbots, other types of chatbots could provide different services, leading the final learning outcomes to become divergent. Each of these applications could have unique benefits, challenges, and implications for learners. By concentrating solely on writing assistance, this paper might not capture the full breadth of AI's impact on the educational domain.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this paper, I examined the influence of Generative AI on learning effectiveness. The results indicated a decrease in learning effectiveness in both AI-based and AI-assisted scenarios, with declines of 25.1% and 12% respectively. Notably, the AI-assisted group exhibited the greatest variance among all groups. Upon evaluating factors that could contribute to enhanced efficiency with AI assistance, I determined that the efficacy of AI-assisted learning is significantly influenced by a student's prior knowledge. These findings have important policy implications: educators need to make students aware of the potential drawbacks of employing AI in their studies. Furthermore, if educators choose to incorporate AI into the curriculum, they must provide clear guidelines on its appropriate use.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding Acknowledgement\u003c/p\u003e\n\u003cp\u003eThe author appreciates the support from Duke Innovation Co-lab.\u003c/p\u003e\n\u003cp\u003eCompeting Interest Declaration\u003c/p\u003e\n\u003cp\u003eThe author declares that he has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003eEthics\u003c/p\u003e\n\u003cp\u003eThis pre-registered experiment follows the protocol at Duke Institutional Review Board.\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eAll data and code are available on OSF.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBian, N., Liu, P., Han, X., Lin, H., Lu, Y., He, B., \u0026amp; Sun, L. (2023). A Drop of Ink Makes a Million Think: The Spread of False Information in Large Language Models (arXiv:2305.04812). arXiv. https://doi.org/10.48550/arXiv.2305.04812\u003c/li\u003e\n\u003cli\u003eChukwuere, J. E. (2023). ChatGPT: The game changer for higher education institutions. Jozac Academic Voice, 3(1), Article 1.\u003c/li\u003e\n\u003cli\u003eDamioli, G., Van Roy, V., \u0026amp; Vertesy, D. (2021). The impact of artificial intelligence on labor productivity. Eurasian Business Review, 11(1), 1\u0026ndash;25. https://doi.org/10.1007/s40821-020-00172-8\u003c/li\u003e\n\u003cli\u003eEloundou, T., Manning, S., Mishkin, P., \u0026amp; Rock, D. (2023). GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models (arXiv:2303.10130). arXiv. https://doi.org/10.48550/arXiv.2303.10130\u003c/li\u003e\n\u003cli\u003eFelten, E. W., Raj, M., \u0026amp; Seamans, R. (2023). How will Language Modelers like ChatGPT Affect Occupations and Industries? (SSRN Scholarly Paper 4375268). https://doi.org/10.2139/ssrn.4375268\u003c/li\u003e\n\u003cli\u003eHailikari, T., Katajavuori, N., \u0026amp; Lindblom-Ylanne, S. (2008). The Relevance of Prior Knowledge in Learning and Instructional Design. American Journal of Pharmaceutical Education, 72(5), 113.\u003c/li\u003e\n\u003cli\u003eKasneci, E., Sessler, K., K\u0026uuml;chemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., G\u0026uuml;nnemann, S., H\u0026uuml;llermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., \u003c/li\u003e\n\u003cli\u003eSailer, M., Schmidt, A., Seidel, T., \u0026hellip; Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274\u003c/li\u003e\n\u003cli\u003eKohnke, L., Moorhouse, B. L., \u0026amp; Zou, D. (2023). ChatGPT for Language Teaching and Learning. RELC Journal, 54(2), 537\u0026ndash;550. https://doi.org/10.1177/00336882231162868\u003c/li\u003e\n\u003cli\u003eLim, F. (2023). Machine-Generated Writing and Chatbots: Nursing Education\u0026rsquo;s Fear of the Unknown. Nursing Education Perspectives, 44(4), 203. https://doi.org/10.1097/01.NEP.0000000000001147\u003c/li\u003e\n\u003cli\u003eMcGee, R. (2023). Using Artificial Intelligence (AI) to Compose a Musical Score for a Taekwondo Tournament Routine: A ChatGPT Experiment. https://doi.org/10.13140/RG.2.2.11235.22569\u003c/li\u003e\n\u003cli\u003eMitrović, S., Andreoletti, D., \u0026amp; Ayoub, O. (2023). ChatGPT or Human? Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPT-generated Text (arXiv:2301.13852). arXiv. https://doi.org/10.48550/arXiv.2301.13852\u003c/li\u003e\n\u003cli\u003eNoy, S., \u0026amp; Zhang, W. (2023a). Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence (SSRN Scholarly Paper 4375283). https://doi.org/10.2139/ssrn.4375283\u003c/li\u003e\n\u003cli\u003eNoy, S., \u0026amp; Zhang, W. (2023b). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187\u0026ndash;192. https://doi.org/10.1126/science.adh2586\u003c/li\u003e\n\u003cli\u003eOguz, F. E., Ekersular, M. N., Sunnetci, K. M., \u0026amp; Alkan, A. (2023). Can Chat GPT be Utilized in Scientific and Undergraduate Studies? Annals of Biomedical Engineering. https://doi.org/10.1007/s10439-023-03333-8\u003c/li\u003e\n\u003cli\u003eOpara, E., Mfon-Ette Theresa, A., \u0026amp; Aduke, T. C. (2023). ChatGPT for Teaching, Learning and Research: Prospects and Challenges (SSRN Scholarly Paper 4375470). https://papers.ssrn.com/abstract=4375470\u003c/li\u003e\n\u003cli\u003ePardos, Z. A., \u0026amp; Bhandari, S. (2023). Learning gain differences between ChatGPT and human tutor generated algebra hints (arXiv:2302.06871). arXiv. https://doi.org/10.48550/arXiv.2302.06871\u003c/li\u003e\n\u003cli\u003ePieters, J. M., Breuer, K., \u0026amp; Simons, P. R.-J. (2012). Learning Environments: Contributions from Dutch and German Research. Springer Science \u0026amp; Business Media.\u003c/li\u003e\n\u003cli\u003eTobias, S. (1994). Interest, Prior Knowledge, and Learning. Review of Educational Research, 64(1), 37\u0026ndash;54. https://doi.org/10.3102/00346543064001037\u003c/li\u003e\n\u003cli\u003evon Rueden, L., Mayer, S., Beckh, K., Georgiev, B., Giesselbach, S., Heese, R., Kirsch, B., Pfrommer, J., Pick, A., Ramamurthy, R., Walczak, M., Garcke, J., Bauckhage, C., \u0026amp; Schuecker, J. (2023). Informed Machine Learning \u0026ndash; A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems. IEEE Transactions on Knowledge and Data Engineering, 35(1), 614\u0026ndash;633. https://doi.org/10.1109/TKDE.2021.3079836\u003c/li\u003e\n\u003cli\u003eYang, C.-H. (2022). How Artificial Intelligence Technology Affects Productivity and Employment: Firm-level Evidence from Taiwan. Research Policy, 51(6), 104536. https://doi.org/10.1016/j.respol.2022.104536\u003c/li\u003e\n\u003cli\u003eZheng, S., Trott, A., Srinivasa, S., Naik, N., Gruesbeck, M., Parkes, D. C., \u0026amp; Socher, R. (2020). The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies (arXiv:2004.13332). arXiv. https://doi.org/10.48550/arXiv.2004.13332\u003c/li\u003e\n\u003cli\u003eZhuo, T. Y., Huang, Y., Chen, C., \u0026amp; Xing, Z. (2023). Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity (arXiv:2301.12867). arXiv. https://doi.org/10.48550/arXiv.2301.12867\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Duke University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"AI, ChatGPT, Education, Productivity, Learning, Behavior","lastPublishedDoi":"10.21203/rs.3.rs-3371292/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3371292/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study, I explored the impact of Generative AI on learning efficacy in academic reading materials using experimental methods. College-educated participants engaged in three cycles of reading and writing tasks. After each cycle, they responded to comprehension questions related to the material. After adjusting for background knowledge and demographic factors, complete reliance on AI for writing tasks led to a 25.1% reduction in accuracy. In contrast, AI-assisted reading resulted in a 12% decline. Interestingly, using AI for summarization significantly improved both quality and output. Accuracy exhibited notable variance in the AI-assisted section. Further analysis revealed that individuals with a robust background in the reading topic and superior reading/writing skills benefitted the most. I conclude the research by discussing educational policy implications, emphasizing the need for educators to warn students about the dangers of over-dependence on AI and provide guidance on its optimal use in educational settings.\u003c/p\u003e","manuscriptTitle":"Experimental Evidence on Negative Impact of Generative AI on Scientific Learning Outcomes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-21 14:09:37","doi":"10.21203/rs.3.rs-3371292/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f17d6671-bd2d-4de7-9ad3-57ab80af7fde","owner":[],"postedDate":"September 21st, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":24872554,"name":"Behavioral Economics"},{"id":24872555,"name":"Artificial Intelligence and Machine Learning"},{"id":24872556,"name":"Social Policy"}],"tags":[],"updatedAt":"2023-09-21T14:09:37+00:00","versionOfRecord":[],"versionCreatedAt":"2023-09-21 14:09:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3371292","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3371292","identity":"rs-3371292","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: preprint-html

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. The paper's references may be in our DB but unresolved to ``paper_id`` (resolution happens at ingest when the cited DOI matches a row we already have). Run the cross-source citation reconcile pass to retry.

Source provenance

europepmc
last seen: 2026-05-19T01:45:01.086888+00:00