Fetching the paper…
Reading the bibliography…
The emergence of large-language models (LLMs) that excel at code generation and commercial products such as GitHub's Copilot has sparked interest in human-AI pair programming (referred to as "pAIr programming") where an AI system collaborates with a human programmer.
Direct manipulation vs. interface agents
Ben Shneiderman and Pattie Maes. 1997 · 1997
Earlier work this paper cites.
Extreme programming explained: embrace change
Kent Beck. 1999 · 1999
Earlier work this paper cites.
The costs and benefits of pair programming
Alistair Cockburn and L Williams. 2001 · 2001
Earlier work this paper cites.
In support of student pair-programming. In Proceedings of the thirty-second SIGCSE technical symposium on Computer Science Education (Charlotte North Carolina USA). ACM, New York, NY, USA
Laurie Williams and Richard L Upchurch. 2001 · 2001
Earlier work this paper cites.
The effects of pair-programming on performance in an introductory programming course. In Proceedings of the 33rd SIGCSE technical symposium on Computer science education (Cincinnati, Kentucky) (SIGCSE ’02) . Association for Computing Machinery, New York, NY, USA, 38–42
Charlie McDowell, Linda Werner, Heather Bullock, and Julian Fernald. 2002 · 2002
Earlier work this paper cites.
In support of pair programming in the introductory computer science course
Laurie Williams, Eric Wiebe, Kai Yang, Miriam Ferzli, and Carol Miller. 2002 · 2002
Earlier work this paper cites.
Improving the CS1 experience with pair programming. In Proceedings of the 34th SIGCSE technical symposium on Computer science education (Reno Navada USA). ACM, New York, NY, USA
Nachiappan Nagappan, Laurie Williams, Miriam Ferzli, Eric Wiebe, Kai Yang, Carol Miller, and Suzanne Balik. 2003 · 2003
Earlier work this paper cites.
Code warriors and code-a-phobes: a study in attitude and pair programming
Lynda Thomas, Mark Ratcliffe, and Ann Robertson. 2003 · 2003
Earlier work this paper cites.
A framework for understanding the factors influencing pair programming success
Mustafa Ally, Fiona Darroch, and Mark Toleman. 2005 · 2005
Earlier work this paper cites.
Factors Affecting the Perceived Effectiveness of Pair Programming in Higher Education
E A Chaparro, Aybala Yuksel, Pablo Romero, and Sallyann Bryant. 2005 · 2005
Earlier work this paper cites.
A Pair Programming Experience
Randall W Jensen. 2005 · 2005
Earlier work this paper cites.
Pair programming productivity: Novice–novice vs. expert–expert
Kim Man Lui and Keith C C Chan. 2006 · 2006
Earlier work this paper cites.
Pair programming improves student retention, confidence, and program quality
Charlie McDowell, Linda Werner, Heather E Bullock, and Julian Fernald. 2006 · 2006
Earlier work this paper cites.
Using collaborative learning research to enhance pair programming pedagogy
David Preston. 2006 · 2006
Earlier work this paper cites.
Evaluating Pair Programming with Respect to System Complexity and Programmer Expertise
Erik Arisholm, Hans Gallis, Tore Dyba, and Dag I K Sjoberg. 2007 · 2007
Earlier work this paper cites.
The Social Dynamics of Pair Programming. In 29th International Conference on Software Engineering (ICSE’07) . ieeexplore.ieee.org, 354–363
Jan Chong and Tom Hurlbutt. 2007 · 2007
Earlier work this paper cites.
Talking the talk: Is intermediate-level conversation the key to the pair programming success story?. In AGILE 2007 . unknown, 84–91
S Freudenberg, Pablo Romero, and Benedict Du Boulay. 2007 · 2007
Earlier work this paper cites.
Animated pedagogical agents: does their degree of embodiment impact learning from static or animated worked examples?
Mary Margaret Lusk and Robert K Atkinson. 2007 · 2007
Earlier work this paper cites.
Pair programming: what’s in it for me?. In Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement (Kaiserslautern Germany). ACM, New York, NY, USA
Andrew Begel and Nachiappan Nagappan. 2008 · 2008
Earlier work this paper cites.
Debugging: A Review of the Literature from an Educational Perspective
Renee McCauley, Sue Fitzgerald, Gary Lewandowski, Laurie Murphy, Beth Simon, Lynda Thomas, and Carol Zander. 2008 · 2008
Earlier work this paper cites.
Integrated and Tool-Supported Teaching of Testing, Debugging, and Verification. In Teaching Formal Methods . Springer Berlin Heidelberg, 125–143
Wolfgang Ahrendt, Richard Bubel, and Reiner Hähnle. 2009 · 2009
Earlier work this paper cites.
Construction and Evaluation of Animated Teachable Agents
Bobby Bodenheimer, B Sanders, M R Kramer, K Viswanath, R Balachandran, Kadira Belynne, and Gautam Biswas. 2009 · 2009
Earlier work this paper cites.
Debugging From the Student Perspective
Sue Fitzgerald, Renée McCauley, Brian Hanks, Laurie Murphy, Beth Simon, and Carol Zander. 2010 · 2009
Earlier work this paper cites.
The Impact of a Peer-Learning Agent Based on Pair Programming in a Programming Course
Keun-Woo Han, Eunkyoung Lee, and Youngjun Lee. 2010 · 2009
Earlier work this paper cites.
Effects of Personality on Pair Programming
Jo E Hannay, Erik Arisholm, Harald Engvik, and Dag I K Sjoberg. 2010 · 2009
Earlier work this paper cites.
The effectiveness of pair programming: A meta-analysis
Jo E Hannay, Tore Dybå, Erik Arisholm, and Dag I K Sjøberg. 2009 · 2009
Earlier work this paper cites.
Investigating the Effect of Pair Programming and Software Size on Software Quality and Programmer Productivity. In 2009 16th Asia-Pacific Software Engineering Conference . 187–193
Raymund Sison. 2009 · 2009
Earlier work this paper cites.
The True Cost of Pair Programming: Development of a Comprehensive Model and Test
W Sun and G Marakas. 2009 · 2009
Earlier work this paper cites.
Do pedagogical agents make a difference to student motivation and learning?
Steffi Heidig and Geraldine Clarebout. 2011 · 2010
Earlier work this paper cites.
Pair debugging: a transactive discourse analysis. In Proceedings of the Sixth international workshop on Computing education research (Aarhus, Denmark) (ICER ’10) . Association for Computing Machinery, New York, NY, USA, 51–58
Laurie Murphy, Sue Fitzgerald, Brian Hanks, and Renée McCauley. 2010 · 2010
Earlier work this paper cites.
Empirical Studies of Pair Programming for CS/SE Teaching in Higher Education: A Systematic Literature Review
Norsaremah Salleh, Emilia Mendes, and John Grundy. 2011 · 2010
Cited alongside, same era.
Pair programming in education: a literature review
Brian Hanks, Sue Fitzgerald, Renée McCauley, Laurie Murphy, and Carol Zander. 2011 · 2011
Cited alongside, same era.
Collaboration in Pair Programming: Driving and Switching. In Agile Processes in Software Engineering and Extreme Programming - 12th International Conference, XP 2011, Madrid, Spain, May 10-13, 2011. Proceedings , Vol. 77. unknown, 43–59
Laura Plonka, Judith Segal, Helen Sharp, and Janet van der Linden. 2011 · 2011
Cited alongside, same era.
An embodiment effect in computer-based learning with animated pedagogical agents
Richard E Mayer and C Scott DaPra. 2012 · 2012
Cited alongside, same era.
“Oh dear stacy!”: social interaction, elaboration, and learning with teachable agents. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Austin, Texas, USA) (CHI ’12) . Association for Computing Machinery, New York, NY, USA, 39–48
Large Language Models are few-shot testers: Exploring LLM-based general bug reproduction
Sungmin Kang, Juyeon Yoon, and Shin Yoo. 2022 · 2022
Later among the works it cites.
Learning Enhancement Using Question-Answer Generation for e-Book Using Contrastive Fine-Tuned T5. In Big Data Analytics . Springer Nature Switzerland, 68–87
Shobhan Kumar, Arun Chauhan, and Pavan Kumar C. 2022 · 2022
Later among the works it cites.
Reading between the lines: Modeling user behavior and costs in AI-assisted programming
Hussein Mozannar, Gagan Bansal, Adam Fourney, and Eric Horvitz. 2022 · 2022
Later among the works it cites.
An Empirical Evaluation of GitHub Copilot’s Code Suggestions
N Nguyen and Sarah Nadi. 2022 · 2022
Later among the works it cites.
Github copilot in the classroom: learning to code with AI assistance
Ben Puryear and Gina Sprint. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Amy Ogan, Samantha Finkelstein, Elijah Mayfield, Claudia D’Adamo, Noboru Matsuda, and Justine Cassell. 2012 · 2012
Cited alongside, same era.
Effective pair programming practice-an experimental study
Venkata Vinod Kumar Padmanabhuni, Hari Praveen Tadiparthi, and Sagar Madina Muralidhar Yanamadala. 2012 · 2012
Cited alongside, same era.
Understanding the impact of Pair Programming on developers attention: A case study on a large industrial experimentation. In 2012 34th International Conference on Software Engineering (ICSE) (Zurich). IEEE, 1094–1101
Alberto Sillitti, Giancarlo Succi, and Jelena Vlasenko. 2012 · 2012
Cited alongside, same era.
Using peer review to teach software testing. In Proceedings of the ninth annual international conference on International computing education research (Auckland, New Zealand) (ICER ’12) . Association for Computing Machinery, New York, NY, USA, 93–98
Joanna Smith, Joe Tessler, Elliot Kramer, and Calvin Lin. 2012 · 2012
Cited alongside, same era.
Animated agents and learning: Does the type of verbal feedback they provide matter?
Lijia Lin, Robert K Atkinson, Robert M Christopherson, Stacey S Joseph, and Caroline J Harrison. 2013 · 2013
Cited alongside, same era.
How Effective are Pedagogical Agents for Learning? A Meta-Analytic Review
Noah L Schroeder, Olusola O Adesope, and Rachel Barouch Gilbert. 2013 · 2013
Cited alongside, same era.
Enhancing collaborative learning using pair programming: Who benefits?
Phil Maguire, Rebecca Maguire, Philip Hyland, and Patrick Marshall. 2014 · 2014
Cited alongside, same era.
Principles based on social cues in multimedia learning: Personalization, voice, image, and embodiment principles
Richard E Mayer. 2014 · 2014
Cited alongside, same era.
Designing PairBuddy—A Conversational Agent for Pair Programming
Peter Robe and Sandeep Kaur Kuttal. 2022 · 2022
Later among the works it cites.
What is it like to program with artificial intelligence?
Advait Sarkar, Andrew D Gordon, Carina Negreanu, Christian Poelitz, Sruti Srinivasa Ragavan, and Ben Zorn. 2022 · 2022
Later among the works it cites.
Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI EA ’22, Article 332) . Association for Computing Machinery, New York, NY, USA, 1–7
Priyan Vaithilingam, Tianyi Zhang, and Elena L Glassman. 2022 · 2022
Later among the works it cites.
Towards Human-Like Educational Question Generation with Large Language Models. In Artificial Intelligence in Education . Springer International Publishing, 153–166
Zichao Wang, Jakob Valdez, Debshila Basu Mallick, and Richard G Baraniuk. 2022 · 2022
Later among the works it cites.
Exploring the Verifiability of Code Generated by GitHub Copilot
Dakota Wong, Austin Kothig, and Patrick Lam. 2022 · 2022
Later among the works it cites.
Assessing the Quality of GitHub Copilot’s Code Generation. In 18th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE ’22)
Burak Yetiştiren, Işik Özsoy, and Eray Tüzün. 2022 · 2022
Later among the works it cites.
Albert Ziegler, Eirini Kalliamvakou, X Alice Li, Andrew Rice, Devon Rifkin, Shawn Simister, Ganesh Sittampalam, and Edward Aftandilian. 2022 · 2022
Later among the works it cites.
How Readable is Model-generated Code? Examining Readability and Visual Inspection of GitHub Copilot. In Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering . Association for Computing Machinery, New York, NY, USA, 1–5
Naser Al Madi. 2023 · 2023
Closest in time.
Programming Is Hard - Or at Least It Used to Be: Educational Opportunities and Challenges of AI Code Generation. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (Toronto ON, Canada) (SIGCSE 2023) . Association for Computing Machinery, New York, NY, USA, 500–506
Brett A Becker, Paul Denny, James Finnie-Ansley, Andrew Luxton-Reilly, James Prather, and Eddie Antonio Santos. 2023 · 2023
Closest in time.
Taking Flight with Copilot: Early insights and opportunities of AI-powered pair-programming tools
Christian Bird, Denae Ford, Thomas Zimmermann, Nicole Forsgren, Eirini Kalliamvakou, Travis Lowdermilk, and Idan Gazit. 2023 · 2023
Closest in time.
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, and Yi Zhang. 2023 · 2023
Closest in time.
Scaffolding CS1 Courses with a Large Language Model-Powered Intelligent Tutoring System. In Companion Proceedings of the 28th International Conference on Intelligent User Interfaces (Sydney, NSW, Australia) (IUI ’23 Companion) . Association for Computing Machinery, New York, NY, USA, 229–232
Chen Cao. 2023 · 2023
Closest in time.
Can Large Language Models Provide Feedback to Students? A Case Study on ChatGPT. (April 2023)
Wei Dai, Jionghao Lin, Flora Jin, Tongguang Li, Yi-Shan Tsai, Dragan Gasevic, and Guanliang Chen. 2023 · 2023
Closest in time.
GitHub Copilot Labs
Github. [n. d.] · 2023
Closest in time.
Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?
John J Horton. 2023 · 2023
Closest in time.
Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming
Majeed Kazemitabaar, Justin Chow, Carl Ka To Ma, Barbara J Ericson, David Weintrop, and Tovi Grossman. 2023 · 2023
Closest in time.
Comparing Code Explanations Created by Students and Large Language Models
Juho Leinonen, Paul Denny, Stephen MacNeil, Sami Sarsa, Seth Bernstein, Joanne Kim, Andrew Tran, and Arto Hellas. 2023 · 2023
Closest in time.
An Engineering Perspective on Writing Assistants for Productivity and Creative Code
Ambar Murillo and Sarah D’Angelo. 2023 · 2023
Closest in time.
Learning gain differences between ChatGPT and human tutor generated algebra hints
Zachary A Pardos and Shreya Bhandari. 2023 · 2023
Closest in time.
The Impact of AI on Developer Productivity: Evidence from GitHub Copilot
Sida Peng, Eirini Kalliamvakou, Peter Cihon, and Mert Demirer. 2023 · 2023
Closest in time.
James Prather, Brent N Reeves, Paul Denny, Brett A Becker, Juho Leinonen, Andrew Luxton-Reilly, Garrett Powell, James Finnie-Ansley, and Eddie Antonio Santos. 2023 · 2023
Closest in time.
CLASS Meet SPOCK: An Education Tutoring Chatbot based on Learning Science Principles
Shashank Sonkar, Lucy Liu, Debshila Basu Mallick, and Richard G Baraniuk. 2023 · 2023
Closest in time.
On AI Anthropomorphism - Human-Centered AI - Medium
Chenhao Tan. 2023 · 2023
Closest in time.
ChatGPT is fun, but not an author
H Holden Thorp. 2023 · 2023
Closest in time.