Timeline of AI in programming

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This is a timeline of AI in programming.

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Year AI subfield Area affected Event type Event description
2023 (October 17) Natural language processing (NLP); educational AI Programming education (Java) Empirical study A study presents preliminary findings on how students interact with AI tools like ChatGPT and GitHub Copilot in introductory Java programming courses. Using a mixed-method design—including quizzes, programming tasks under different support conditions, and interviews—the study highlights the diverse attitudes and behaviors students display toward AI assistance. While tools like ChatGPT offer flexibility and reduce hesitation in seeking help, concerns remain about their impact on developing core programming skills. The findings offer valuable insights for integrating AI in education responsibly.[1]
2023 (December 31) Machine learning; deep learning; natural language processing (NLP); expert systems Software engineering lifecycle Systematic literature review An article systematically reviews 110 studies to assess how AI has been integrated into software engineering over the past decade. It highlights the widespread application of AI techniques—especially machine learning, deep learning, natural language processing, optimization algorithms, and expert systems—across all phases of the software development life cycle. Key benefits include improved defect prediction, code recommendation, automated requirement analysis, and maintenance precision. The review emphasizes the need for interpretable and ethical AI tools to ensure responsible advancement in software engineering.[2]
2024 (February 6) Machine learning AI systems development, applied software engineering Research publication A paper examines the rapid progress and societal implications of AI and machine learning (ML). It outlines AI’s core capabilities—such as learning, problem-solving, and decision-making—and ML’s role in enabling systems to improve through data analysis. The paper explores real-world applications including natural language processing, image and speech recognition, and autonomous vehicles. It also addresses potential risks, such as job displacement and misuse of technology. Emphasizing the importance of ethics, the study advocates for responsible AI development to balance innovation with minimizing harm to individuals and society.[3]
2024 (March 22) Generative artificial intelligence The article explores whether artificial intelligence will replace programmers, concluding that AI will augment rather than eliminate programming roles. Instructors Norman McEntire and James Gappy from UC San Diego Extended Studies explain that generative AI, despite its power to automate coding, debugging, and optimization, still relies on human oversight, creativity, and technical understanding. They emphasize the importance of mastering fundamentals, using AI as a collaborator, and maintaining continuous learning to stay relevant. Programmers who effectively integrate AI tools into their workflow will be more productive, adaptable, and valuable. Ultimately, AI is framed as an assistant—not a replacement—for coders.[4]
2024 (May 9) Software engineering with AI assistance; machine learning AI-assisted programming and code collaboration Research publication An article examines the use of AI-pair programming—collaborative coding between human developers and AI assistants—at TiMi Studio, a prominent game development company. Analyzing data from code repositories, reviews, surveys, and interviews, the study finds that AI-pair programming enhances code quality and developer satisfaction. Benefits include time-saving, error reduction, skill development, and better feedback. However, challenges such as trust issues, lack of explainability, and reduced autonomy also emerge. The paper offers practical insights for optimizing AI-pair programming in real-world software development environments.[5]
2024 (June 16) Large language models (LLMs); natural language processing; code generation Experimental study An article examines how large language models (LLMs) like GPT and Codex affect programmer productivity and behavior. In a study with 24 participants completing Python tasks, researchers compare three setups: GitHub Copilot (auto-complete), GPT-3 (conversational), and traditional tools (web browser). Results show that AI-assisted coding significantly boosts productivity and alters coding strategies. The study highlights how interaction design (autocomplete vs. conversational) influences user engagement and problem-solving approaches. Overall, the research underscores the transformative impact of LLMs on programming and the need to optimize their integration in development workflows.[6]
2024 (September 12) A study by economists from MIT, Princeton, and the University of Pennsylvania find that AI coding assistants like GitHub Copilot boost developer productivity by 26% in enterprise environments. Analyzing data from 4,800 developers at Microsoft, Accenture, and another Fortune 100 firm, the research shows a 13.5% rise in code commits and a 38.4% increase in compilation frequency, with no decline in code quality. Junior developers benefit most, improving output by up to 40%. The study emphasizes gradual adoption, training, and governance as key to maximizing AI’s benefits while avoiding overreliance and integration challenges.[7]
2024 (October 5) Large language models (LLMs); code generation A study investigates the impact of AI coding tools on novice programming education in a first-semester course with 73 engineering student teams over 12 weeks. Using surveys and qualitative reports, it finds that AI tool familiarity rose from 28% to 100%, with increasing student satisfaction. Students primarily used AI for writing code comments (91.7%), debugging (80.2%), and information seeking (68.5%). The tools enhanced learning and improved the perceived real-world relevance of programming. However, concerns emerged regarding potential cheating, over-reliance on AI, and weaker grasp of core programming concepts, highlighting the need for balanced and guided AI integration in education.[8]
2024 (November 25) Applied artificial intelligence; machine learning; software engineering automation Software development life cycle Academic publication An article examines how AI is transforming the software development life cycle. It highlights AI’s applications in areas such as design, coding, testing, project management, and maintenance, emphasizing its role in automating tasks, improving efficiency, and enhancing code quality. The paper also discusses key challenges, including over-reliance on AI tools, ethical dilemmas, and security issues. Looking ahead, it explores emerging trends like adaptive systems, AI-enhanced team collaboration, and fully automated software development. Overall, the study underscores AI’s profound and growing influence on the future of software engineering.[9]
2024 (December 3) Generative artificial intelligence Programming education and AI-assisted learning Academic publication A study evaluates the impact of the GenAI Gemini tool on programming education in a polytechnic university in Guayaquil, Ecuador. Using a quantitative, quasi-experimental design, it finds that AI integration significantly enhances student motivation, interest, and satisfaction. Notably, 91% of students report increased enthusiasm for programming, and 90% feel their learning expectations were met or exceeded. The research highlights GenAI's potential to transform teaching but stresses the importance of proper educator training, ethical guidance for students, sustained engagement, and curriculum alignment to harness its full benefits.[10]
2024 (December 8) Educational artificial intelligence; intelligent tutoring systems; learning analytics Programming education policy and strategy Academic publication A study reviews the role of AI in transforming education. It highlights AI’s growing application in areas like intelligent tutoring, automated grading, and learning analytics, driven by the need for personalized learning. While acknowledging various challenges and limitations, the study emphasizes AI’s potential to create more efficient and intelligent education systems. Programming education is identified as especially crucial, fostering students’ logical thinking, creativity, and social engagement. The paper proposes strategic guidance for integrating AI in education and underscores its relevance for shaping future talent and educational policy.[11]
2024 (December 23) Generative artificial intelligence Software development, AI-assisted programming Academic publication An article envisions how AI will reshape software engineering by the end of the decade. It contrasts current AI-assisted tools like GitHub Copilot and ChatGPT with projected advancements, forecasting a shift in developers’ roles—from manual coders to coordinators of AI-driven ecosystems. The study introduces the concept of HyperAssistant, a future AI tool designed to enhance coding, debugging, collaboration, and even mental health support. Rather than replacing developers, AI is seen as a powerful partner, enhancing software quality, efficiency, and creativity in a transformed development landscape.[12]
2025 (February 20) Commentary A New York Times article argues that generative AI is transforming, rather than replacing, software developers. Tools like GitHub Copilot now assist with debugging, documentation, and translation, improving productivity by up to 30%. While entry-level hiring has weakened, demand for experienced developers and AI literacy is rising. Experts predict AI will automate most code writing, shifting programmers’ roles toward design, oversight, and creative problem-solving. Training programs are adapting at the time, emphasizing core computer science, critical thinking, and the ability to guide AI-driven development.[13]
2025 (March 24) Commentary An article by Adlene Sifi explores how generative AI, particularly tools like GitHub Copilot, enhances developer experience (DevEx)—the overall satisfaction, productivity, and well-being of software developers. It explains that DevEx depends on company culture, processes, collaboration, and tools, and can be improved through faster feedback loops, lower cognitive load, and better flow states.[14]
2025 (May) A study examines how 231 students in an “Object-Oriented Programming” course use AI chatbots like ChatGPT and how this relates to their academic performance. The study concludes that most students use AI for debugging and code comprehension, but few rely on it weekly, indicating limited dependency. Students value AI’s speed but criticize its errors and inconsistencies. The study finds a negative correlation between frequent AI use and grades, suggesting weaker students depend more on AI tools. Researchers conclude that unstructured AI use may hinder learning and urge educators to guide critical, reflective integration of AI into coursework.[15]
2025 (June 5) A Coursera article concludes that AI will not replace programmers in the near future, though it is reshaping their work. Generative AI tools can automate repetitive coding, assist with debugging, documentation, and forecasting, but still lack creativity, critical thinking, and reliability. These limitations—such as hallucinated code, security, and copyright risks—mean human oversight remains essential. According to the article, AI may reduce entry-level positions but create new roles in AI development and supervision. Long-term replacement is constrained by trust and societal acceptance. Programmers can future-proof their careers by mastering AI, ML, prompt engineering, and related technologies.[16]
2025 (July 10) A study by the AI research nonprofit METR finds that advanced AI coding assistants can slow down experienced software developers rather than accelerate their work. In experiments using the tool Cursor on familiar open-source projects, seasoned programmers complete tasks 19% slower when aided by AI. Participants had expected a 24% speedup and still believe they worked faster, despite results showing otherwise. Researchers express surprise, noting they had predicted a “2x speed up.” The findings question assumptions that AI consistently boosts productivity and highlight challenges in human–AI collaboration in software development.[17]
2025 (August 9) A Reuters investigation finds that artificial intelligence is accelerating the decline of coding bootcamps, once a key entry point into software engineering. As AI tools automate programming tasks and eliminate many entry-level developer roles, job prospects for recent graduates have sharply diminished. Placement rates at bootcamps like Codesmith fell from 83% in 2021 to 37% in 2023. Venture investors and educators cite market saturation and shifting employer needs, but AI is now seen as the “final blow.” The industry’s collapse reflects a broader trend: shrinking demand for junior coders and rising pay for elite AI researchers.[18]

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References

  1. ↑ Maher, Mary Lou; Tadimalla, Sharvani Y.; Dhamani, Dhruva (17 October 2023). An Exploratory Study on the Impact of AI Tools on the Student Experience in Programming Courses: An Intersectional Analysis Approach. pp. 1–5. doi:10.1109/fie58773.2023.10343037. Retrieved 4 June 2025.
  2. ↑ Durrani, Usman; Akpınar, Mehmet; Adak, Mustafa Furkan; Kabakuş, Ahmet Talha; Öztürk, Mustafa Murat; Saleh, Mohammed (31 December 2023). "A Decade of Progress: A Systematic Literature Review on the Integration of AI in Software Engineering Phases and Activities (2013–2023)". IEEE Access. 1. doi:10.1109/access.2024.3488904. Retrieved 4 June 2025.
  3. ↑ Rana, Sohel (6 February 2024). "Exploring the Advancements and Ramifications of Artificial Intelligence". Deleted Journal. 2 (1): 30–35. doi:10.60087/jaigs.v2i1.p35. Retrieved 4 June 2025.
  4. ↑ “Extended Studies Blog” (10 October 2024). "Will AI Replace Programmers? Navigating the Future of Coding". UC San Diego Extended Studies. Retrieved 4 November 2025.
  5. ↑ Chen, Tianyi (9 May 2024). "The Impact of AI-Pair Programmers on Code Quality and Developer Satisfaction: Evidence from TiMi Studio". doi:10.1145/3665348.3665383. Retrieved 4 June 2025. {{cite journal}}: Cite journal requires |journal= (help)
  6. ↑ Weber, Thomas; Brandmaier, Maximilian; Schmidt, Albrecht; Mayer, Sven (16 June 2024). "Significant Productivity Gains through Programming with Large Language Models". Proceedings of the ACM on Human-Computer Interaction. 8 (EICS): 1–29. doi:10.1145/3661145. Retrieved 4 June 2025.
  7. ↑ Brown, Leah (12 September 2024). "New Research Reveals AI Coding Assistants Boost Developer Productivity by 26%: What IT Leaders Need to Know". IT Revolution. Retrieved 6 November 2025.
  8. ↑ Zviel-Girshin, Rina (2024). "The Good and Bad of AI Tools in Novice Programming Education". Education Sciences. 14 (10): 1089. doi:10.3390/educsci14101089. Retrieved 4 June 2025.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  9. ↑ Zhang, Q. (25 November 2024). "The Role of Artificial Intelligence in Modern Software Engineering". Applied and Computational Engineering. 97 (1): 18–23. doi:10.54254/2755-2721/97/20241339. Retrieved 4 June 2025.
  10. ↑ Llerena-Izquierdo, Joe; Méndez Reyes, Johan; Ayala Carabajo, Raquel; Andrade Martínez, César Miguel (3 December 2024). "Innovations in Introductory Programming Education: The Role of AI with Google Colab and Gemini". Education Sciences. 14 (12). Multidisciplinary Digital Publishing Institute. doi:10.3390/educsci14121330. Retrieved 4 June 2025.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  11. ↑ Wang, Xing-lian (8 December 2024). "Application and Impact of Artificial Intelligence in Education: A Case Study of Programming Education". Lecture Notes in Education Psychology and Public Media. 74 (1). EWA Publishing: 182–187. doi:10.54254/2753-7048/2024.bo17948. Retrieved 4 June 2025.
  12. ↑ Qiu, Ketai; Puccinelli, Niccolò; Ciniselli, Matteo; Di Grazia, Luca (23 December 2024). "From Today's Code to Tomorrow's Symphony: The AI Transformation of Developer's Routine by 2030". ACM Transactions on Software Engineering and Methodology. doi:10.1145/3709353. Retrieved 4 June 2025.
  13. ↑ Lohr, Steve (20 February 2025). "A.I. Is Prompting an Evolution, Not Extinction, for Coders". The New York Times. Retrieved 6 November 2025.
  14. ↑ Sifi, Adlene (24 March 2025). "How does generative AI impact Developer Experience?". Microsoft Developer Blog. Retrieved 6 November 2025.
  15. ↑ Lepp, Marina; Kaimre, Joosep (2025). "Does generative AI help in learning programming: Students' perceptions, reported use and relation to performance". Computers in Human Behavior Reports. 18: 100642. doi:10.1016/j.chbr.2025.100642. {{cite journal}}: |access-date= requires |url= (help)
  16. ↑ "Will AI Replace Programmers and Software Engineers?". Coursera. 5 June 2025. Retrieved 5 November 2025.
  17. ↑ Tong, Anna (10 July 2025). "AI slows down some experienced software developers, study finds". Reuters. Retrieved 4 November 2025.
  18. ↑ Tong, Anna (9 August 2025). "From bootcamp to bust: How AI is upending the software development industry". Reuters. Retrieved 6 November 2025.
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