Programming education and learner motivation in the age of generative AI: student and educator perspectives
| Date | 10 July 2024 |
| Pages | 91-109 |
| DOI | https://doi.org/10.1108/ILS-10-2023-0163 |
| Published date | 10 July 2024 |
| Author | Samuel Boguslawski,Rowan Deer,Mark G. Dawson |
Programming education and
learner motivation in the age of
generative AI: student and
educator perspectives
Samuel Boguslawski,Rowan Deer and Mark G. Dawson
CODE University of Applied Sciences, Berlin, Germany
Abstract
Purpose –Programmingeducation is being rapidly transformed by generativeAI tools and educators must
determine how best to support students in this context. This study aims to explore the experiences of
programmingeducators and students to inform future educationprovision.
Design/methodology/approach –Twelve students and six members of faculty in a small technology-
focused university were interviewed. Thematic analysis of the interview data was combined with data collected
from a survey of 44 students at the same university. Self-determination theory was applied as an analytical
framework.
Findings –Threethemeswereidentified –bespoke learning, affect and support –that significantly impact
motivation and learning outcomes in programming education. It was also found that students are already
making extensive use of large language models (LLMs). LLMs can significantly improve learner autonomy and
sense of competence by improving the options for bespoke learning; fostering emotions that are conducive to
engendering and maintaining motivation; and inhibiting the negative affective states that discourage learning.
However, current LLMs cannot adequately provide or replacesocial support, which is still a key factor in learner
motivation.
Research limitations/implications –Integratingthe use of LLMs into curricula can improvelearning
motivation and outcomes. It can also free educators from certain tasks, leaving them with more time and
capacity to focus their attention on developing social learning opportunities to further enhance learner
motivation.
Originality/value –To the best of the authors’knowledge, this is the first attempt to explore the
relationshipbetween motivation and LLM use in programmingeducation.
Keywords Programming education, Large language models, LLMs, Generative AI, ChatGPT,
Copilot, Introductory programming, Learning science, Self-determination theory, Motivation
Paper type Research paper
1. Introduction
The latest development of the digital revolution is the advent of generative AI, or large
language models (LLMs).Just as the computer revolution of the 1960s transformed countless
© Samuel Boguslawski, Rowan Deer and Mark G. Dawson. Published by Emerald Publishing Limited.
This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may
reproduce, distribute, translate and create derivative works of this article (for both commercial and non-
commercial purposes), subject to full attribution to the original publication and authors. The full terms of
this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
This research was funded by the German Ministry for Education and Research (BMBF).
Age of
generative AI
91
Received31 October 2023
Revised27 February 2024
27May 2024
Accepted28 May 2024
Informationand Learning
Sciences
Vol.126 No. 1/2, 2025
pp. 91-109
EmeraldPublishing Limited
2398-5348
DOI 10.1108/ILS-10-2023-0163
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2398-5348.htm
sectors of society –including manufacturing, communication and education –ongoing
digitization and generative AI are set to radically transform how we live, work and learn.
While industry actors must quickly adopt new technologies to stay ahead, educational
institutions can be slower to integrate advances into their teaching (Gilch et al.,2019),
meaning that students may leave university unable to meet industry expectations (Craig
et al., 2018). Recognizing this, the German Government has invested in various efforts to
digitize education (BMBF, 2021), including funding the development of an online learning
platform for programming. As partof this latter project, researchers at CODE University of
Applied Sciences investigated the experiences of programming students and educators to
inform the platform design.
What emerged was a general picture of contemporary programming students’
motivations, emotions, needs and preferences –all of which significantly impact learning.
Initially, the researchers did not aim to explore LLM use, but it quickly became clear that
this has already become an integral resource for many learners. While educators are still
debating the pros, cons and impacts of LLMs on programming education (Lau and Guo,
2023), they have already impacted programming students’learning strategies and
motivation, with significantimplications for education design going forward.
2. Literature review
As with all learning, motivation is key to success in programming education. In the
following three subsections, we review the literature around learner motivation and self-
determination theory (SDT), before moving on to discuss how this relates to programming
education. Finally,we discuss the early research on LLM use in programmingeducation.
2.1 Learner motivation and self-determination theory
Intrinsic and extrinsic motivation both significantly impact learners’engagement and
performance (Jones, 2013). Intrinsic motivation refers to the internal enjoyment and
satisfaction of engaging in an activity, whereas extrinsic motivation refers to external
factors that drive individuals to do so, often involving receiving rewards or avoiding
punishments (Schwartz and Wrzesniewski,2019). Intrinsic motivation is recognized as key
to effective learning (Renniger and Hidi, 2019). In the context of programming education,
this involves curiosity about coding and the satisfaction derived from completing
programming tasks. Extrinsic motivation is provided by external factors such as deadlines,
assessments or the promise of career opportunities. It is generally accepted that intrinsic
motivation leads to betterengagement and performance (Vansteenkiste et al.,2004), whereas
extrinsic motivation can inhibit intrinsic motivation in certain circumstances (Deci and
Ryan, 2000;Murayama, 2019). However,because intrinsic and extrinsic motivations coexist
and can interact in complex ways (Cerasoli et al.,2014), it is important to take both into
account. Holistically, motivation can be fostered by linking external motivators to more
intrinsic drives –for example, by framing learning in terms of learners’future goals
(Vansteenkiste et al., 2004)and underlining the usefulness of computational thinking skills –
while tangible records of achievement foster self-perceptions of capability (Eckerdal et al.,
2005;Demertzi et al.,2018).
According to SDT, motivation is significantly influenced by the basic human need to
experience autonomy, competence and relatedness (Deci and Ryan, 2000). Autonomy is a
sense of agency, control and independence; competence involves feeling capable, confident
and effective; and relatedness involves feeling connected to others and social structures. A
learner might increasetheir sense of autonomy by choosing when, how and what they learn;
believe they have improved in competence as they successfully completeactivities; and feel
ILS
126,1/2
92
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