This study seeks to deepen the understanding of the direct and indirect effects of human–computer dialogic interaction programming activities, facilitated by ChatGPT, on student engagement. Data were collected from 109 Chinese high school students who engaged in programming tasks using either ChatGPT-driven dialogic interaction or traditional pair programming. A quasi-experimental analysis revealed that ChatGPT-based programming activities remarkably boost student engagement, outperforming pair programming in behavioral, cognitive, and emotional dimensions. Results demonstrated that such activities help minimize off-task behaviors, promote higher-order cognitive skills, and foster greater interest in programming. Additionally, these interactions enhance students’ self-efficacy and reduce learning anxiety. The findings underscore the potential of ChatGPT-driven dialogic interaction in programming education. This study offers practical recommendations to enhance student engagement in programming learning.
Al-AbdullatifA. M.AlsubaieM. A. (2024). ChatGPT in learning: Assessing students’ use intentions through the lens of perceived value and the influence of AI literacy. Behavioral Sciences, 14(9), 845. https://doi.org/10.3390/bs14090845
2.
AnS.ZhangS. (2024). Effects of ability grouping on students’ collaborative problem solving patterns: Evidence from lag sequence analysis and epistemic network analysis. Thinking Skills and Creativity, 51(3), Article 101453. https://doi.org/10.1016/j.tsc.2023.101453
3.
AndersonN. D. (2016). A call for computational thinking in undergraduate psychology. Psychology Learning and Teaching, 15(3), 226–234. https://doi.org/10.1177/1475725716659252
4.
BansalG.ChamolaV.HussainA.GuizaniM.NiyatoD. (2024). Transforming conversations with AI—a comprehensive study of ChatGPT. Cognitive Computation, 16(5), 2487–2510. https://doi.org/10.1007/s12559-023-10236-2
5.
BettayebA. M.Abu TalibM.Sobhe AltayasinahA. Z.DakalbabF. (2024). Exploring the impact of ChatGPT: Conversational AI in education. Frontiers in Education, 9(7), 796. https://doi.org/10.3389/feduc.2024.1379796
6.
BianN.HanX.SunL.LinH.LuY.HeB.JiangS.DongB. (2024). ChatGPT is a knowledgeable but inexperienced solver: An investigation of commonsense problem in large language models. arXiv:2303.16421. https://doi.org/10.48550/arXiv.2303.16421
7.
BileA. (2022). Development of intellectual and scientific abilities through game-programming in Minecraft. Education and Information Technologies, 27(5), 7241–7256. https://doi.org/10.1007/s10639-022-10894-z
8.
BondM. (2020). Facilitating student engagement through the flipped learning approach in K-12: A systematic review. Computers & Education, 151, 103819. https://doi.org/10.1016/j.compedu.2020.103819
9.
BowmanN. A.JarrattL.CulverK.SegreA. M. (2021). The impact of pair programming on college students’ interest, perceptions, and achievement in computer science. ACM Trans. Comput. Educ, 21(3), 1–19. https://doi.org/10.1145/3440759
10.
BrennanK.ResnickM. (2012). New frameworks for studying and assessing the development of computational thinking.
BucaioniA.EkedahlH.HelanderV.NguyenP. T. (2024). Programming with ChatGPT: How far can we go?Machine Learning with Applications, 15(1), Article 100526. https://doi.org/10.1016/j.mlwa.2024.100526
13.
CalcagniE.AhmedF.Trigo-ClapésA. L.KershnerR.HennessyS. (2023). Developing dialogic classroom practices through supporting professional agency: Teachers’ experiences of using the T-SEDA practitioner-led inquiry approach. Teaching and Teacher Education, 126, 104067. https://doi.org/10.1016/j.tate.2023.104067
14.
ChangH.LiuB.ZhaoY.LiY.HeF. (2024). Research on the acceptance of ChatGPT among different college student groups based on latent class analysis. Interactive Learning Environments, 33(1), 22–38. https://doi.org/10.1080/10494820.2024.2331646
15.
ChenY.-C.HouH.-T. (2024). A mobile contextualized educational game framework with ChatGPT interactive scaffolding for employee ethics training. Journal of Educational Computing Research, 62(7), 1737–1762. https://doi.org/10.1177/07356331241268505
16.
DawsonJ. (2017). Analysing quantitative survey data for business and management students. Sage Publications Ltd. https://doi.org/10.4135/9781473983311
17.
DemirÖ.SeferogluS. S. (2021). A comparison of solo and pair programming in terms of flow experience, coding quality, and coding achievement. Journal of Educational Computing Research, 58(8), 1448–1466. https://doi.org/10.1177/0735633120949788
18.
DennyP.LeinonenJ.PratherJ.Luxton-ReillyA.AmaroucheT.BeckerB. A.ReevesB. N. (2024). Prompt problems: A new programming exercise for the generative AI era. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1. Association for Computing Machinery, 296–302. https://doi.org/10.1145/3626252.3630909
19.
FanG.LiuD.ZhangR.PanL. (2025). The impact of AI-assisted pair programming on student motivation, programming anxiety, collaborative learning, and programming performance: A comparative study with traditional pair programming and individual approaches. International Journal of STEM Education, 12(1), 16. https://doi.org/10.1186/s40594-025-00537-3
20.
GharbaouiH.MansouriK.PoirierF. (2024). Improving student engagement and success in computer programming courses through social learning in online environments. International Journal of Engineering Pedagogy (iJEP), 14(6), 54–68. https://doi.org/10.3991/ijep.v14i6.48705
21.
GhimireA.EdwardsJ. (2024). Coding with AI: How are tools like ChatGPT being used by students in foundational programming courses. In OlneyA. M.ChountaI. A.LiuZ.SantosO. C.BittencourtI. I. (Eds.), Artificial intelligence in education. AIED 2024 Lecture Notes in Computer Science (Vol 14830, pp. 259–267). Springer. https://doi.org/10.1007/978-3-031-64299-9_20
22.
GongX.LiZ.QiaoA. (2024). Impact of generative AI dialogic feedback on different stages of programming problem solving. Education and Information Technologies. https://doi.org/10.1007/s10639-024-13173-1
23.
GroothuijsenS.van den BeemtA.RemmersJ. C.van MeeuwenL. W. (2024). AI chatbots in programming education: Students’ use in a scientific computing course and consequences for learning. Computers and Education: Artificial Intelligence, 7, 100290. https://doi.org/10.1016/j.caeai.2024.100290
24.
HaindlP.WeinbergerG. (2024). Students’ experiences of using ChatGPT in an undergraduate programming course. IEEE Access, 12, 43519–43529. https://doi.org/10.1109/ACCESS.2024.3380909
25.
HartleyK.HayakM.KoU. H. (2024). Artificial intelligence supporting independent student learning: An evaluative case study of ChatGPT and learning to code. Education Sciences, 14(2), 120. https://doi.org/10.3390/educsci14020120
26.
HawlitschekA.BerndtS.SchulzS. (2023). Empirical research on pair programming in higher education: A literature review. Computer Science Education, 33(3), 400–428. https://doi.org/10.1080/08993408.2022.2039504
27.
HopcanS.PolatE.AlbayrakE. (2022). Collaborative behavior patterns of students in programming instruction. Journal of Educational Computing Research, 60(4), 1035–1062. https://doi.org/10.1177/07356331211062260
28.
HuescaG.Martínez-TreviñoY.Molina-EspinosaJ. M.Sanromán-CallerosA. R.Martínez-RománR.Cendejas-CastroE. A.BustosR. (2024). Effectiveness of using ChatGPT as a tool to strengthen benefits of the flipped learning strategy. Education Sciences, 14(6). 6. https://doi.org/10.3390/educsci14060660
29.
JingY.WangH.ChenX.WangC. (2024). What factors will affect the effectiveness of using ChatGPT to solve programming problems? A quasi-experimental study. Humanities and Social Sciences Communications, 11(1), 319–412. https://doi.org/10.1057/s41599-024-02751-w
30.
JoštG.TaneskiV.KarakatičS. (2024). The impact of large language models on programming education and student learning outcomes. Applied Sciences, 14(10), 4115. https://doi.org/10.3390/app14104115
31.
KilicS.GokogluS.OzturkM. (2021). A valid and reliable scale for developing programming-oriented computational thinking. Journal of Educational Computing Research, 59(2), 257–286. https://doi.org/10.1177/0735633120964402
32.
KosarT.OstojićD.LiuY. D.MernikM. (2024). Computer science education in ChatGPT era: Experiences from an experiment in a programming course for novice programmers. Mathematics, 12(5), 629. https://doi.org/10.3390/math12050629
33.
KrizsanT.LambicD. (2024). Examining the impact of pair programming on efficiency, motivation, and stress among students of different skills and abilities in lower grades in elementary schools. Education and Information Technologies, 29(18), 25257–25280. https://doi.org/10.1007/s10639-024-12859-w
34.
Lafuente MartinezM.LevequeO.BenitezI.HardebolleC.ZuffereyJ. D. (2022). Assessing computational thinking: Development and validation of the algorithmic thinking test for adults. Journal of Educational Computing Research, 60(6), 1436–1463. https://doi.org/10.1177/07356331211057819
35.
LaiX.WongG. K. (2022). Collaborative versus individual problem solving in computational thinking through programming: A meta-analysis. British Journal of Educational Technology, 53(1), 150–170. https://doi.org/10.1111/bjet.13157
36.
LeeH.-Y.ChenP.-H.WangW.-S.HuangY.-M.WuT.-T. (2024). Empowering ChatGPT with guidance mechanism in blended learning: Effect of self-regulated learning, higher-order thinking skills, and knowledge construction. International Journal of Educational Technology in Higher Education, 21(1), 16. https://doi.org/10.1186/s41239-024-00447-4
37.
LeeH.-Y.WuT.-T.LinC.-J.WangW.-S.HuangY.-M. (2024). Integrating computational thinking into scaffolding learning: An innovative approach to enhance science, technology, engineering, and mathematics hands-on learning. Journal of Educational Computing Research, 62(2), 431–467. https://doi.org/10.1177/07356331231211916
38.
LeiY.FuX.ZhaoJ.YiB. (2024). The effect of ability grouping on students' computational thinking in shared regulation-supported collaborative programming. Education and Information Technologies, 30(2), 1417–1443. https://doi.org/10.1007/s10639-024-12838-1
39.
LiC.SunD.XuJ.ZhuY.HuangY.ZhengW.TangX.LiY. (2024). Exploring interactive behaviours of urban and rural teachers in blended synchronous classrooms: Insights from a proposed interaction analysis framework. Computers & Education, 222, 105152. https://doi.org/10.1016/j.compedu.2024.105152
40.
LiQ.JiangQ.LiangJ.-C.XiongW.ZhaoW. (2024). Engagement predicts computational thinking skills in unplugged activity: Analysis of gender differences. Thinking Skills and Creativity, 52, 101537. https://doi.org/10.1016/j.tsc.2024.101537
41.
LiW.HuangJ.-Y.LiuC.-Y.TsengJ. C. R.WangS.-P. (2023). A study on the relationship between student’ learning engagements and higher-order thinking skills in programming learning. Thinking Skills and Creativity, 49(2), Article 101369. https://doi.org/10.1016/j.tsc.2023.101369
42.
LiaoJ.ZhongL.ZheL.XuH.LiuM.XieT. (2024). Scaffolding computational thinking with ChatGPT. IEEE Transactions on Learning Technologies, 17, 1628–1642. https://doi.org/10.1109/TLT.2024.3392896
43.
LiuJ.LiS. (2024). Toward artificial intelligence-human paired programming: A review of the educational applications and research on artificial intelligence code-generation tools. Journal of Educational Computing Research, 62(5), 1385–1415. https://doi.org/10.1177/07356331241240460
44.
LoC. K.HewK. F.JongM. S. (2024). The influence of ChatGPT on student engagement: A systematic review and future research agenda. Computers & Education, 219, 105100. https://doi.org/10.1016/j.compedu.2024.105100
45.
MaB.ChenL.KonomiS. (2024). Enhancing programming education with ChatGPT: A case study on student perceptions and interactions in a Python course. arXiv:2403.15472. https://doi.org/10.48550/arXiv.2403.15472
46.
MarwanS.GaoG.FiskS.PriceT. W.BarnesT. (2020). Adaptive immediate feedback can improve novice programming engagement and intention to persist in computer science. In Proceedings of the 2020 ACM conference on international computing education research, 194–203. ICER ’20: International computing education research conference. Association for Computing Machinery. https://doi.org/10.1145/3372782.3406264
47.
OyelereA. S.AgboF. J.OyelereS. S. (2023). Formative evaluation of immersive virtual reality expedition mini-games to facilitate computational thinking. Computers & Education: X Reality, 2(5), Article 100016. https://doi.org/10.1016/j.cexr.2023.100016
48.
ParkA.KimT. (2025). Code suggestions and explanations in programming learning: Use of ChatGPT and performance. International Journal of Management in Education, 23(2), Article 101119. https://doi.org/10.1016/j.ijme.2024.101119
49.
Peracaula-BoschM.González-MartínezJ. (2024). Towards a hermeneutics of computational thinking in wing’s approximations. Journal of Educational Computing Research, 61(8), 139–158. https://doi.org/10.1177/07356331231193142
50.
PopoviciM.-D. (2024). ChatGPT in the classroom. Exploring its potential and limitations in a functional programming course. International Journal of Human-Computer Interaction, 40(22), 7743–7754. https://doi.org/10.1080/10447318.2023.2269006
51.
PrasadA.ChaudharyK.SharmaB. (2022). Programming skills: Visualization, interaction, home language and problem solving. Education and Information Technologies, 27(3), 3197–3223. https://doi.org/10.1007/s10639-021-10692-z
52.
QureshiB. (2023). Exploring the use of ChatGPT as a tool for learning and assessment in undergraduate computer science curriculum: Opportunities and challenges. arXiv:2304.11214. https://doi.org/10.48550/arXiv.2304.11214
53.
RahmanM. M.WatanobeY. (2023). ChatGPT for education and research: Opportunities, threats, and strategies. Applied Sciences, 13(9), 5783. https://doi.org/10.3390/app13095783
54.
RospigliosiA. (2023). Artificial intelligence in teaching and learning: What questions should we ask of ChatGPT?Interactive Learning Environments, 31(1), 1–3. https://doi.org/10.1080/10494820.2023.2180191
55.
SchererR.SiddiqF.Sánchez ViverosB. (2020). A meta-analysis of teaching and learning computer programming: Effective instructional approaches and conditions. Computers in Human Behavior, 109, Article 106349. https://doi.org/10.1016/j.chb.2020.106349
56.
SilvaC. A. G. daRamosF. N.de MoraesR. V.SantosE. L. dos. (2024). ChatGPT: Challenges and benefits in software programming for higher education. Sustainability, 16(3), 1245. https://doi.org/10.3390/su16031245
57.
SmiderleR.RigoS. J.MarquesL. B.Peçanha de Miranda CoelhoJ. A.JaquesP. A. (2020). The impact of gamification on students’ learning, engagement and behavior based on their personality traits. Smart Learning Environments, 7(1), 3. https://doi.org/10.1186/s40561-019-0098-x
58.
StrzeleckiA.CichaK.RizunM.RuteckaP. (2024). Acceptance and use of ChatGPT in the academic community. Education and Information Technologies, 29(17), 22943–22968. https://doi.org/10.1007/s10639-024-12765-1
59.
SuY.-S.WangS.LiuX. (2024). Using epistemic network analysis to explore primary school students’ computational thinking in pair programming learning. Journal of Educational Computing Research, 62(2), 559–593. https://doi.org/10.1177/07356331231210560
60.
SunD.BoudouaiaA.YangJ.XuJ. (2024). Investigating students’ programming behaviors, interaction qualities and perceptions through prompt-based learning in ChatGPT. Humanities and Social Sciences Communications, 11(1), 1447–1514. https://doi.org/10.1057/s41599-024-03991-6
61.
SunD.BoudouaiaA.ZhuC.LiY. (2024). Would ChatGPT-facilitated programming mode impact college students’ programming behaviors, performances, and perceptions? An empirical study. International Journal of Educational Technology in Higher Education, 21(1), 14. https://doi.org/10.1186/s41239-024-00446-5
62.
SunL.LiuJ.LiuY. (2024). Comparative experiment of the effects of unplugged and plugged-in programming on computational thinking in primary school students: A perspective of multiple influential factors. Thinking Skills and Creativity, 52(8), Article 101542. https://doi.org/10.1016/j.tsc.2024.101542
63.
TanJ.WuL.MaS. (2024). Collaborative dialogue patterns of pair programming and their impact on programming self-efficacy and coding performance. British Journal of Educational Technology, 55(3), 1060–1081. https://doi.org/10.1111/bjet.13412
64.
TheodoropoulosA.LepourasG. (2021). Augmented reality and programming education: A systematic review. International Journal of Child-Computer Interaction, 30(4), Article 100335. https://doi.org/10.1016/j.ijcci.2021.100335
65.
TikvaC.TambourisE. (2021). Mapping computational thinking through programming in K-12 education: A conceptual model based on a systematic literature review. Computers & Education, 162(1), Article 104083. https://doi.org/10.1016/j.compedu.2020.104083
66.
TsaiC.-W.LinM. Y.-C.ChengY.-P.LeeL.-Y.ChyrW.-L.LinC.-H.LinJ.-W.TsaiM.-C. (2023). The effects of online peer-facilitated learning and distributed pair programming on students’ learning. Computers & Education, 203(1), Article 104849. https://doi.org/10.1016/j.compedu.2023.104849
67.
TsaiM.-J.LiangJ.-C.HsuC.-Y. (2021). The computational thinking scale for computer literacy education. Journal of Educational Computing Research, 59(4), 579–602. https://doi.org/10.1177/0735633120972356
68.
UrbanM.DěchtěrenkoF.LukavskýJ.HrabalováV.SvachaF.BromC.UrbanK. (2024). ChatGPT improves creative problem-solving performance in university students: An experimental study. Computers & Education, 215, 105031. https://doi.org/10.1016/j.compedu.2024.105031
69.
Van PouckeM. (2024). ChatGPT, the perfect virtual teaching assistant? Ideological bias in learner-chatbot interactions. Computers and Composition, 73(3), Article 102871. https://doi.org/10.1016/j.compcom.2024.102871
70.
WangM.-T.FredricksJ. A.YeF.HofkensT. L.LinnJ. S. (2016). The math and science engagement scales: Scale development, validation, and psychometric properties. Learning and Instruction, 43, 16–26. https://doi.org/10.1016/j.learninstruc.2016.01.008
71.
WeiX.LinL.MengN.TanW.KongS.-C.Kinshuk. (2021). The effectiveness of partial pair programming on elementary school students’ Computational Thinking skills and self-efficacy. Computers & Education, 160, 104023. https://doi.org/10.1016/j.compedu.2020.104023
72.
WengX.YeH.DaiY.NgO. (2024). Integrating artificial intelligence and computational thinking in educational contexts: A systematic review of instructional design and student learning outcomes. Journal of Educational Computing Research, 62(6), 1640–1670. https://doi.org/10.1177/07356331241248686
73.
WongZ. Y.LiemG. A. D. (2022). Student engagement: Current state of the construct, conceptual refinement, and future research directions. Educational Psychology Review, 34(1), 107–138. https://doi.org/10.1007/s10648-021-09628-3
74.
WuT.-T.LeeH.-Y.LiP.-H.HuangC.-N.HuangY.-M. (2024). Promoting self-regulation progress and knowledge construction in blended learning via ChatGPT-based learning aid. Journal of Educational Computing Research, 61(8), 3–31. https://doi.org/10.1177/07356331231191125
75.
XieW.LinX. (2025). Are dialogic teaching-and-learning programs effective in pre-primary and primary schools? A meta-analysis. Teaching and Teacher Education, 153, 104823. https://doi.org/10.1016/j.tate.2024.104823
76.
XuF.CorreiaA.-P. (2024a). Adopting distributed pair programming as an effective team learning activity: A systematic review. Journal of Computing in Higher Education, 36(2), 1–30. https://doi.org/10.1007/s12528-023-09356-3
77.
XuF.CorreiaA.-P. (2024b). Measuring mutual engagement in the context of middle-school pair programming: Development and validation of a self-reported questionnaire. Computers in Human Behavior Reports, 14(1), Article 100415. https://doi.org/10.1016/j.chbr.2024.100415
78.
XuW.WuY.OuyangF. (2023). Multimodal learning analytics of collaborative patterns during pair programming in higher education. Int J Educ Technol High Educ, 20(1), 8. https://doi.org/10.1186/s41239-022-00377-z
79.
YangA. C. M.LinJ.-Y.LinC.-Y.OgataH. (2024). Enhancing python learning with PyTutor: Efficacy of a ChatGPT-Based intelligent tutoring system in programming education. Computers and Education: Artificial Intelligence, 7(2), Article 100309. https://doi.org/10.1016/j.caeai.2024.100309
80.
YangT.-C.LinZ.-S. (2024). Enhancing elementary school students’ computational thinking and programming learning with graphic organizers. Computers & Education, 209(7), Article 104962. https://doi.org/10.1016/j.compedu.2023.104962
81.
Yildiz DurakH. (2018). Flipped learning readiness in teaching programming in middle schools: Modelling its relation to various variables. Journal of Computer Assisted Learning, 34(6), 939–959. https://doi.org/10.1111/jcal.12302
82.
Yildiz DurakH. (2020). The effects of using different tools in programming teaching of secondary school students on engagement, computational thinking and reflective thinking skills for problem solving. Technology, Knowledge and Learning, 25(1), 179–195. https://doi.org/10.1007/s10758-018-9391-y
83.
YilmazR.Karaoglan YilmazF. G. (2023a). The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4(2), Article 100147. https://doi.org/10.1016/j.caeai.2023.100147
84.
YilmazR.Karaoglan YilmazF. G. (2023b). Augmented intelligence in programming learning: Examining student views on the use of ChatGPT for programming learning. Computers in Human Behavior: Artificial Humans, 1(2), Article 100005. https://doi.org/10.1016/j.chbah.2023.100005
85.
YolcuV.DemirerV. (2023). The effects of educational robotics in programming education on students’ programming success, computational thinking, and transfer of learning. Computer Applications in Engineering Education, 31(6), 1633–1647. https://doi.org/10.1002/cae.22664
86.
YunG.LeeK. M.ChoiH. H. (2025). Empowering student learning through artificial intelligence: A bibliometric analysis. Journal of Educational Computing Research, 62(8), 2042–2075. https://doi.org/10.1177/07356331241278636
87.
YürümO. R.Taşkaya-TemizelT.YıldırımS. (2023). The use of video clickstream data to predict university students’ test performance: A comprehensive educational data mining approach. Education and Information Technologies, 28(5), 5209–5240. https://doi.org/10.1007/s10639-022-11403-y
88.
ZhangX.ChenY.HuL.BaoY.TuY.-F.HwangG.-J. (2024). A metaphor-based robot programming approach to facilitating young children’s computational thinking and positive learning behaviors. Computers & Education, 215, 105039. https://doi.org/10.1016/j.compedu.2024.105039
89.
ZhaoL.LiuX.WangC.SuY.-S. (2022). Effect of different mind mapping approaches on primary school students’ computational thinking skills during visual programming learning. Computers & Education, 181(1), Article 104445. https://doi.org/10.1016/j.compedu.2022.104445
90.
ZhongX.ZhanZ. (2024). An intelligent tutoring system for programming education based on informative tutoring feedback: System development, algorithm design, and empirical study. Interactive Technology and Smart Education, 22(1), 3–24. https://doi.org/10.1108/ITSE-09-2023-0182
91.
ZhouR.LiY.HeX.JiangC.FangJ.LiY. (2024). Understanding undergraduates’ computational thinking processes: Evidence from an integrated analysis of discourse in pair programming. Education and Information Technologies, 29(15), 19367–19399. https://doi.org/10.1007/s10639-024-12597-z
92.
ZhouY.ChaiC. S.LiX.MaC.LiB.YuD.LiangJ.-C. (2023). Application of metacognitive planning scaffolding for the cultivation of computational thinking. Journal of Educational Computing Research, 61(6), 1123–1142. https://doi.org/10.1177/07356331231160294
