Timeline of AI in programming

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This is a timeline of AI in programming.

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Year AI subfield Programming domain Event type Event description
2023 (October 17) 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) 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) 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 (May 9) 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.[4]
2024 (June 16) 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.[5]
2024 (October 5) 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.[6]
2024 (November 25) 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.[7]
2024 (December 3) 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.[8]
2024 (December 8) 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.[9]
2024 (December 23) 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.[10]

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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. ↑ 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)
  5. ↑ 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.
  6. ↑ 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)
  7. ↑ 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.
  8. ↑ 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)
  9. ↑ 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.
  10. ↑ 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.
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