International Journal of Instruction

October 2025 ● Vol.18, No.4
p-ISSN: 1694-609X

e-ISSN: 1308-1470 ● www.e-iji.net

pp. 601-626
Article submission code:
20250325192042

Received: 25/03/2025
Revision: 13/06/2025

Accepted: 22/06/2025
OnlineFirst: 07/07/2025

Motivational Theories in Action: A Guide for Teaching Artificial
Intelligence Prompts to Support Student Learning Motivation
Shiva Hajian
Faculty of Psychology, Kwantlen Polytechnic University, Surrey, BC, Canada,
shiva.hajian@kpu.ca
Daniel H. Chang
Faculty of Education, Simon Fraser University, Burnaby, BC, Canada, dth7@sfu.ca
Quincy Q. Wang
Faculty of Education,
quincy_wang@sfu.ca

Simon

Fraser

Michael Pin-Chuan Lin
Faculty of Education,
michael.lin@msvu.ca

Mount

Saint

University,

Vincent

Burnaby,

University,

BC,

Canada,

Halifax,

Canada,

This conceptual study explores how motivational theories can guide the use of
generative Artificial Intelligence (AI) tools, such as ChatGPT, to enhance student
learning motivation. Drawing on Self-Determination Theory (SDT), ExpectancyValue Theory (EVT), and Mindset Theory (MT), we introduce the Motivation
Construction Model (MCM), a theoretical framework consisting of three
interrelated phases: contemplation, goal setting & planning, and action. We
demonstrate how MCM can be applied in AI-driven learning environments to
support personalized prompts, targeted feedback, and adaptive guidance to
motivate learning. We propose that MCM is a strategic and holistic approach to
equip educators with actionable guidelines to use AI for motivating students while
adhering to ethical pedagogical principles. Although the MCM framework is
grounded in established motivational theories, its real-world application remains to
be explored. Future research should examine the effectiveness of MCM in
authentic classroom contexts to better understand its potential for enhancing
student motivation and informing evidence-based instructional practices.
Keywords: motivation, self-determination theory, expectancy-value theory, mindset
theory, artificial intelligence (AI), Generative AI, ChatGPT, prompting in ChatGPT
INTRODUCTION
In teaching and learning environments, student engagement, persistence, and
achievement are key indicators of motivation (Martin, 2008; Saeed & Zyngier, 2012).
Motivated learners actively participate in educational activities, stay focused on
Citation: Hajian, S., Chang, D. H., Wang, Q. Q., & Lin, M. P-C. (2025). Motivational theories in
action: A guide for teaching artificial intelligence prompts to support student learning motivation.
International Journal of Instruction, 18(4), 601-626. https://doi.org/10.29333/iji.2025.18433a

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challenging tasks, and persist despite obstacles. This connection between motivation
and learning is evident in students’ choices to engage with educational opportunities,
their sustained effort in completing assignments, and their resilience in overcoming
difficulties (Guay, 2022; Jang, 2008; Sedden & Clark, 2016). Understanding how
motivation develops and is maintained is therefore essential for designing effective
learning environments that support student academic success.
Motivation is shaped by a wide range of factors, including individual characteristics,
sociocultural influences, contextual conditions, instructional support, and the
availability of learning resources (e.g., Alvandoudi et al., 2023; Chen, 2023; Hidajat et
al., 2020; Munawaroh et al., 2022; Nugraha et al., 2021; Suanto et al., 2023; Tiang-uan,
2024). Many educators recognize the importance of initiating, directing, and sustaining
goal-oriented learning behaviours (Murphy & Alexander, 2000; Radil et al., 2023).
However, there is still a lack of theory-based practical strategies to effectively nurture
motivation and address the challenges that arise in diverse learning environments (Lai,
2011; Patrick, 2023; Radil et al., 2023). A search for “motivation” reveals numerous
definitions, with each theory offering a unique perspective to understand this complex
construct. For example, some theories emphasize factors such as physiological needs as
well as external and internal reinforcements, while others mostly consider personal
beliefs, cultural values, and social expectations (Pekrun & Marsh, 2022). Despite this
multiplicity of definitions and functions, motivation has always been universally
conceptualized as an innate desire that drives individuals to participate in an activity or
become involved in a decision or a plan (Lai, 2011; Motha & Lin, 2014). Lai (2011)
argues that “motivation involves a constellation of beliefs, perceptions, values, interests,
and actions that are all closely related” (p.5).
Numerous studies (e.g., Maslow, 1943; Ryan & Deci, 2017; Weiner, 1985; Wigfield &
Eccles, 2000; Yeager & Dweck, 2020) have enriched our understanding of motivation
by exploring various assumptions and strategic approaches in the context of learning.
Early theories of motivation predominantly focused on behavioural factors, needs, and
instincts. For example, Hull’s Drive Theory (Hull, 1943) proposed that behaviour is
primarily driven by the need to satisfy biological urges and enhance survival, while
Maslow’s Hierarchy of Needs (Maslow, 1954) suggested that individuals must first
fulfill basic, lower-order needs before being motivated to pursue higher-order goals
(Lester et al., 1983).
With the emergence of the cognitive revolution in the latter half of the 20th century, the
science of learning shifted its focus toward conscious cognitive processes (Dember,
1974). This paradigmatic shift led scholars to explore motivation more comprehensively
by focusing on factors such as autonomy, goal setting, self-efficacy beliefs, and
available learning opportunities for growth and mastery (Miller & Brickman, 2004;
Ryan et al., 2019). Researchers began to investigate how setting specific, challenging
goals can enhance motivation, how a learner’s belief in their own abilities (selfefficacy) influences their motivation and performance, and how different instructional
environments can either support or hinder motivational processes. Notable theories that
have emerged from this approach include Self-Determination Theory (Benware & Deci,
1984; Deci & Ryan, 2012; Ryan & Deci, 2000, 2020), Expectancy-Value Theory

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(Atkinson, 1957; Eccles et al., 1983; Eccles & Wigfield, 2002; Wigfield & Eccles,
2000), and Mindset Theory (Dweck, 2016; Dweck & Leggett, 1988).
This article integrates three key motivational theories, Self-Determination Theory
(SDT), Expectancy-Value Theory (EVT), and Mindset Theory (MT), to develop a
practical framework for fostering student learning motivation. While motivational
theories offer valuable insights into the complexities of motivation for instructional
purposes, research indicates that they often lack specific and practical recommendations
for educators, particularly in the context of AI-oriented learning environments (Patrick,
2023; Radil et al., 2023; Williamson & Eynon, 2020). Therefore, we propose applicable
guidelines that integrate insights from motivational theories with actionable strategies
employing generative AI tools such as ChatGPT to address this need. Our suggested
guidelines, which we present later, are specifically designed to help educators construct
learners’ motivation by promoting key factors such as autonomy, mastery, relatedness,
growth mindset, and self-efficacy through personalization. These personalization
configurations include delivering timely feedback, providing dynamic content, and
creating adaptive assessments using appropriate AI-assisting prompts that can be
explicitly taught by instructors. Research has shown that AI-supported personalized
learning plans improve student self-regulation and academic success by providing
adaptive learning experiences, targeted interventions, predictive analytics for
performance assessment, and dynamic content delivery (Chang et al., 2023; Lin &
Chang, 2020; Ng et al., 2024; Vorst & Jelicic, 2019).
To provide a clear overview of the paper’s structure and objectives, we begin by
introducing the key theoretical perspectives of three motivational theories, followed by
a brief review of the role of AI in education. Next, we integrate these theories using a
conceptual synthesis approach and propose the Motivation Construction Model (MCM),
which consists of three phases: contemplation, goal setting and planning, and action.
Finally, we provide practical guidelines based on the MCM, demonstrating how
educators can design and implement AI prompts to harness the full potential of
generative AI such as ChatGPT to enhance student motivation and improve learning
outcomes. Our comprehensive review not only highlights the potential of generative AI
in education but also offers actionable insights for educators seeking to integrate
motivational theories into their teaching practices.
Part I: Theoretical Foundations of Motivation
Self-Determination Theory (SDT)
According to self-determination theory proposed by Ryan and Deci (Deci & Ryan,
1985, 2012; Ryan & Deci, 2000, 2017), intrinsic motivation is significantly influenced
by the fulfillment of three fundamental psychological needs: autonomy, competence,
and relatedness.
Autonomy, the first of these psychological needs, refers to the feeling of having choice
and controlling over one’s own behaviours and goals. When individuals experience a
sense of autonomy, they feel empowered and capable of making choices aligned with
their values and preferences. Autonomy-driven motivation enhances engagement with

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tasks and activities, as individuals are more likely to invest effort and persevere when
they feel a sense of personal agency (Reeve et al., 2004; Ryan & Deci, 2017).
The second psychological need, competence, emphasizes the innate human tendency to
pursue mastery and proficiency in various skills and tasks. When individuals are given
opportunities to develop skills, acquire relevant experience, and experience meaningful
progress, their intrinsic motivation becomes strengthened. The pursuit of competence
not only enhances the individual’s confidence and efficacy but also contributes to a
heightened enjoyment of the activities they undertake (Deci & Ryan, 2012).
The third psychological need, relatedness, centers around the fundamental human desire
to connect with others and cultivate a sense of belonging. Fulfilling this need involves
forming meaningful relationships and experiencing a sense of connection within social
groups. This sense of connection acts as a powerful catalyst for personal and
psychological growth and motivates individuals to explore their interests, take on
challenges, and pursue their goals with enthusiasm and determination (Ryan & Deci,
2000; Derakhshan & Noughabi, 2024).
It is crucial to recognize that these three needs are interconnected and mutually
reinforcing, creating a synergistic effect on an individual’s overall well-being and
motivation. The SDT, through its emphasis on these psychological needs, provides
valuable insights into understanding and cultivating intrinsic motivation across diverse
contexts, ranging from educational settings to workplace environments and personal
pursuits.
Research has consistently demonstrated the critical role of instructors in supporting
student autonomy and competence (Black & Deci, 2000; Yang et al., 2022). This role
involves equipping students with essential domain-specific and inquiry-based
knowledge, as well as providing appropriate feedback to help them solve problems and
tackle challenges in their own unique ways (Black & Deci, 2000; Hajian Moghadam,
2021; Hajian et al., 2019, 2021; Orsini et al., 2015). However, implementing this
student-centered approach in traditional classrooms can be difficult due to factors such
as limited time, resources, and support (Mosier, 2018).
AI has the potential to enhance learner intrinsic motivation in alignment with the
principles of SDT. For example, AI technologies provide personalized instruction and
feedback that support students in learning independently, developing self-regulation
skills, and taking ownership of their learning (Chang et al., 2023; Dai et al., 2023; Lin
& Chang, 2023). In addition, AI fosters the development of competence by offering
adaptive learning experiences and real-time performance feedback (Choung et al.,
2023). Furthermore, AI enables social platforms and virtual environments to foster
meaningful connections and a sense of relatedness among users (Zhai et al., 2021).
Expectancy-Value Theory (EVT)
Why do students have variability in goal setting, problem-solving approaches, and
perceptions of achievement? And why do some learners display higher levels of
intrinsic motivation than others? According to EVT, these differences largely arise from
two fundamental psychological constructs: expectancy and value (Atkinson, 1957;

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Eccles et al., 1983; Eccles & Wigfield, 2002; Wigfield & Eccles, 2000; Loh, 2019). An
individual’s motivation is strongly influenced by the value they attribute to a task and
their belief in achieving desirable outcomes and expected rewards.
The theory breaks down task value into four parts: how important it is for individuals to
do well (attainment value), how much they enjoy it (intrinsic value), how useful it is for
future goals (utility value), and whether it presents any obstacles or conflicts (cost)
(Leaper, 2011). According to this model, beliefs about success and task value are
influenced by personal factors, such as one’s abilities, past experiences, and goals and
external factors, such as culture, social standards, and norms. Research shows that
students are more likely to engage in academic tasks or pursue learning goals when they
perceive them as achievable (Bong, 2004; Schunk & DiBenedetto, 2020). When
individuals find a goal achievable and have confidence in their ability to achieve the
desired outcome, they are more motivated to take action and work towards that goal
(Schunk & DiBenedetto, 2020). The interplay between expectancy and value
significantly impacts learning motivation, academic performance, and activity choices
(Atkinson, 1957; Wigfield & Ponnock, 2020).
Several studies propose that students’ perceptions of negative consequences associated
with task completion, termed as cost, also affect academic outcomes (Flake et al., 2015;
Y. Jiang et al., 2018; Madigan & Curran, 2020). Eccles et al. (1983) argued that
expectancies primarily relate to one’s self-perceived ability, perceptions of task
difficulty, expectations from others, and level of control. Task value components are
categorized into intrinsic value (based on interest), attainment value (importance of
performing well), and utility value (integral to future goals). Cost, acting as a mediator,
influences perceived effort (students’ perception of the effort required for task success),
opportunity costs (when engaging in one activity prevents participation in other valued
activities), and psychological costs of failure (anxiety related to potential task failure).
Cost serves as a negative motivational factor that can significantly diminish the overall
value students assign to a task (Flake et al., 2015).
We argue that generative AI tools can play an important role in enhancing learners’
expectancy and task value by providing personalized insights, real-world applications of
academic content, and adaptive support. AI can increase expectancy by offering tailored
explanations, scaffolded prompts, and instant feedback that help students develop
competence and confidence in their abilities. Moreover, generative AI may increase
task value by connecting learning materials to students’ interests, future career
aspirations, and real-world challenges. In the practical guidance section, we will
demonstrate a series of AI prompts designed to exemplify how these interventions can
effectively address pertinent motivational issues among learners.
Mindset Theory (MT)
Carol Dweck’s Mindset Theory (Dweck, 2016; Dweck, 1986) proposes two primary
mindsets: a fixed mindset, where individuals believe their abilities are innate and
unchangeable, leading to a desire to appear competent without much effort; and a
growth mindset, where individuals believe abilities can be developed through
dedication and hard work. Dweck’s research emphasizes the impact of these mindsets

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on motivation, resilience, and overall achievement, with the idea that fostering a growth
mindset can positively influence learning outcomes by encouraging a belief in the
malleability of intelligence and the value of effort in the learning process. MT or the
implicit theory of intelligence (Dweck & Leggett, 1988) argues that our beliefs about
intelligence and personal abilities can have a profound influence on our approach
towards learning motivation and academic achievement. For example, if a student’s
primary goal is to achieve high grades in a science course as a way to gain external
validation of their abilities, their motivation may decrease once they have reached that
goal. This decline in motivation toward performance goals (i.e., short-term target
performance objectives) often occurs when the desired level of performance is
significantly challenged throughout the process.
Dweck and Leggett (1988) noticed that children responding to a challenge revealed two
patterns of performance: mastery-oriented and helpless responses. The helpless
response often was characterized by avoidance of challenges and difficulty facing
obstacles. In contrast, the mastery-oriented pattern involved seeking challenging tasks
and persistence after failure (Dweck & Yeager, 2021; Yeager & Dweck, 2020). In a
study conducted by Burnette et al. (2018), it was demonstrated that a growth mindset
intervention had a significant positive impact on academic performance as the students
who received the intervention exhibited noticeable improvements in motivation and
self-efficacy. The mindset that learners adopt significantly influences how they
approach challenges, setbacks, and learning opportunities (Campbell et al., 2020;
Kapasi & Pei, 2022). It also shapes their resilience and impacts their academic
achievement.
We believe generative AI tools can foster a growth mindset by providing targeted
support and encouragement across different subjects. For example, ChatGPT can frame
challenges as opportunities to apply knowledge, but students must learn how to ask the
right questions to receive meaningful insights. By teaching students to use AI prompts
strategically, instructors help them persist through challenges, view setbacks as part of
learning, and develop stronger problem-solving skills. This approach not only enhances
students’ understanding of the material but also reinforces the belief that effort and
continuous learning are essential for success.
Part II: The Role of AI in Education
Current research on Generative AI in education emphasizes its transformative potential
while also highlighting challenges that need to be addressed for academic purposes
(Chang et al., 2023; Uppal & Hajian, 2025). AI applications in education have shown
potential to facilitate personalized learning, increase efficiency, enhance student
engagement and motivation, and support students in developing a growth mindset
(Harry, 2023; Luckin & Holmes, 2016; Neji et al., 2023). These advancements are
evident in areas such as adaptive content delivery, automated learner profiling, and
intelligent user interfaces, all of which contribute to more tailored and interactive
learning experiences (Barrera Castro et al., 2024; Tahiru, 2021).
However, one notable gap in the field is the lack of research on human-AI collaboration
and learner control within educational contexts (Ji et al., 2022; Echeverria et al., 2020).

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While significant progress has been made in advancing personalized learning and
intelligent tutoring systems, limited studies have investigated how students can
collaboratively engage with AI to enhance learning outcomes (Brusilovsky, 2024). For
example, a framework for integrating self-regulated learning (SRL) principles, such as
goal setting and feedback, have been proposed but remain underexplored in practice
(Chang et al., 2023). Similarly, the conceptualization of AI as a collaborative “learning
mate” requires further empirical validation to ensure its scalability and effectiveness
(Kim et al., 2022). Moreover, educators’ competencies in guiding students to use AI
tools remain a pressing concern (Barrera Castro et al., 2024). While AI’s contributions
to personalization and engagement are well-documented and theorized, its capacity to
foster intrinsic motivation through theoretical frameworks is underdeveloped (Barrera
Castro et al., 2024; Zhai et al., 2021).
The potential of AI to support and sustain motivation over time remains largely
underexplored due to the absence of a structured, theory-driven approach. While
curriculum design and content development provide foundational support (Kickert et
al., 2022), they do not fully address how AI can actively help construct motivation in a
student-centered learning environment. A key gap in current research is the lack of
explicit strategies that empower students to use AI tools effectively, particularly through
well-designed prompts that promote deeper engagement, autonomy, and mastery (Neji
et al., 2023). Without clear instructional support, students risk interacting with AI
passively rather than as an active learning partner. To address this gap, a theoretical
framework is needed to integrate motivational principles into AI-supported learning and
provide instructors with practical strategies to guide students to engage meaningfully
with appropriate AI tools.
Part III: Developing a Motivational Model for Effective AI Integration
Rationale for Integrating SDT, EVT, and MT
Research indicates that existing motivational theories remain fragmented and lack a
cohesive structure for application in learning contexts (Martin, 2008, 2023; Bembenutty
et al., 2022). While SDT, EVT, and MT each offer valuable insights into different
aspects of motivation (Figure 1), none of them fully capture the diverse and dynamic
process through which student motivation is constructed. Each theory is limited in
isolation: SDT emphasizes autonomy and intrinsic needs, EVT focuses on expectancy
and task value, and MT highlights the role of beliefs about intelligence and persistence.
However, motivation is a dynamic process that evolves in phases and is influenced by
various factors such as personal beliefs, goal orientations, self-regulation skills, and
both internal and external incentives (Walter & Hart, 2009; Williams & Williams,
2011).
To bridge these gaps, the Motivation Construction Model (MCM) integrates SDT, EVT,
and MT into a cohesive framework that explains how motivation is initiated, sustained,
and regulated. Aligned with Dörnyei’s (2000) argument that motivation governs choice,
persistence, and effort, MCM structures motivation into three phases: contemplation,
goal setting & planning, and action. This integrated approach moves beyond isolated
theories to provide a structured model that helps educators design learning

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environments that actively support student learning motivation at every stage – from
initial engagement to sustained effort and the completion of action.

Figure 1
Summary of Motivational Theories and the Associated Learning Contexts.
Note. This diagram provides a concise summary of three key motivational theories – SDT, EVT,
and MT– and outlines several effective learning strategies derived from these theories.
Additionally, it highlights how these theories inform certain learning contexts that can take place
in today’s classrooms. This integration of theory and learning scenarios drives the development of
our motivation construction framework.

MCM for Enhancing Motivation in AI-Driven Learning Contexts
Despite the wealth of research on various motivational theories, one of the greatest
challenges in education remains translating these theories into effective classroom
practice (Van der Putten, 2017). This challenge is further amplified in today’s AI-driven
classrooms, where Generative AI (GAI) presents new opportunities for engagement,
personalized feedback, and adaptive learning (Chiu et al., 2023). AI-powered tools have
the potential to enhance student motivation by providing just-in-time, theory-driven
support tailored to individual learners (Chang et al., 2023; Fauzi et al., 2023; Lin &
Chang, 2023). For instance, AI chatbots can analyze student responses, offer
constructive feedback, and generate adaptive prompts, ensuring that students remain
appropriately challenged yet capable of success (Chang et al., 2023; Chiu et al., 2023).
AI can also facilitate self-regulated learning through reverse prompting, a process in
which students initiate a task, and the AI provides subsequent guidance. This approach
has been shown to enhance learning engagement (Chang et al., 2023) and sustain
motivation (Mohamed et al., 2024).

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While AI offers potential benefits, educators currently lack a structured framework to
effectively integrate AI into learning environments that foster student motivation.
Without clear guidance, AI tools may be underutilized or misaligned with pedagogical
goals, leading to inconsistent support for student motivation (Chiu et al., 2023). While
AI can enhance motivation (Mohame et al., 2024; Neji et al., 2023), it does not
inherently explain how to construct and sustain it across different phases of learning.
We developed the MCM, to bridge the gap between motivational theories, AI tools, and
classroom implementation. The MCM provides educators with a cohesive framework
that explains how motivation is built over time and how AI can be strategically
integrated to support each phase. Rather than using AI in an aimlessly fragmented way,
MCM enables educators to purposefully design AI-driven interventions that promote
autonomy, competence, and persistence in learning. By adopting MCM, educators can
effectively enhance motivation while ensuring that AI technological tools are grounded
in established motivational principles. This approach not only addresses the longstanding disconnect between theory and practice but also equips educators with a
practical, theory-backed model for fostering motivation in promoting AI-enhanced
education.
MCM Phases and the Role of AI-Generated Prompts in Enhancing Each Phase
To address how educators can effectively cultivate motivation of learning for learners,
we propose the MCM depicted in Figure 2. This practically oriented model comprises
three interacting phases: (a) Contemplation, (b) Goal setting & Planning, and (c)
Action. Within the MCM, each phase plays a dynamic and interactive role in shaping
individuals’ motivation throughout the learning process. Here, “dynamic” refers to the
continuous interactions and mutual influences among the three phases. For example,
ineffective goal setting can lead students to revisit the contemplation and action phases
to reassess and adjust their approach. When goals are not clearly defined or realistic,
students may struggle to make progress, leading to frustration and disengagement.
Revisiting these phases provides an opportunity to reflect on initial intentions, identify
shortcomings, and refine goals to be more realistic. This iterative process of
reassessment and adjustment is crucial for developing effective motivational strategies.
This back-and-forth interaction is expected to create a continuous cycle of motivation
and improvement. By presenting these phases, the MCM offers a structured framework
that supports individuals’ motivation to initiate, sustain, and complete learning
activities. We advocate for this process-oriented motivational approach, as we believe it
holds substantial practical value for educators in effectively supporting learners’
motivation throughout their educational journey.

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Figure 2
Motivation construction model (MCM)
Note. The MCM consists of three interacting phases: (a) Contemplation, (b) Goal setting &
Planning, and (c) Action. Each phase influences the others, creating a continuous cycle of
motivation and improvement. This model provides a practical framework for educators to
effectively support and enhance learners’ motivation throughout their learning process.

Contemplation
Contemplation, as the initial step in this model, offers individuals an opportunity to
evaluate their current situation and recognize areas where change may be desired or
needed. This concept aligns with the definition provided by Kuhn and Lao (1998), who
described contemplation as engaging in mental activities related to learning a specific
topic. When learners engage in contemplation, they exercise autonomy by exploring
their own thoughts, feelings, and aspirations independently, without external pressure or
influence (aligning with the autonomy aspect of SDT). Contemplative practices allow
students to shift their mindset and put more effort into activities they perceive as
valuable and expect to succeed in. When learners contemplate the value and
significance of an activity, they are more likely to perceive it as personally meaningful.
This perception, in turn, can lead to greater intrinsic motivation and effort, as suggested
by EVT. Additionally, the contemplation phase allows learners to strengthen their selfefficacy by reflecting on their past achievements and imagining future successes. This
process aligns with the principles of MT, as it encourages learners to develop a positive
belief in their abilities and embrace challenges with confidence. Together, these theories

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highlight the importance of contemplation in shaping learners’ motivations and
behaviours toward achieving academic success.
During this phase, AI-generated prompts should focus on helping learners identify their
needs and explore possibilities. Specifically, prompts should encourage help-seeking
behaviours, such as asking for clarification on concepts, seeking clarity by breaking
down complex ideas, strategy requests for approaching learning tasks, resource
exploration to discover relevant materials, problem-solving guidance to tackle
challenges, and skill enhancement by identifying areas for growth.
Goal setting and planning
A goal, as conceptualized by Fishbach and Ferguson (Fishbach & Ferguson, 2007), is
essentially a mental representation of a desired state of an outcome. A goal encapsulates
learners’ cognitive vision of how they hope for things to unfold or the specific outcome
they aspire to achieve. In educational contexts, goal setting is defined as the process of
establishing clear and productive targets, or objectives, for teaching and learning
(Moeller et al., 2012). Therefore, a goal serves as a guiding framework that influences
learners’ thoughts, actions, and decisions, and shapes their behaviour as they work
towards it. This cognitive aspect emphasizes the importance and value of learners’
efforts and aspirations toward particular outcomes. Studies have shown that appropriate
goal setting, along with appropriate, timely, and specific feedback, can lead to higher
achievement, better performance, and a higher level of self-efficacy, self-regulation, and
motivation in learners (Hajian Moghadam, 2021; Hajian et al., 2019, 2021; Hidajat et
al., 2020; Moeller et al., 2012). Planning, on the other hand, involves developing a
systematic approach or roadmap to reach goals. Through effective planning goals are
broken down into smaller, manageable tasks, resources are used effectively, and
timelines are established. The relationship between goal setting and planning lies in
their mutual reinforcement: goal setting provides the target, while planning outlines the
steps needed to reach it. Without effective planning, goals may remain distant
aspirations lacking a concrete path forward. Similarly, without clearly defined goals,
planning efforts may lack direction and coherence (Locke & Latham, 2006).
Students benefit from support and guidance during goal setting and planning for several
reasons. First, many students may lack experience or confidence in setting realistic and
achievable goals, requiring assistance to identify meaningful objectives aligned with
their abilities and aspirations. Additionally, guidance from educators (or knowledgeable
others) helps students navigate the complexities of planning, such as brainstorming
effective strategies, evaluating previous strategies, breaking down large goals into
manageable tasks and prioritizing activities. Furthermore, support during goal setting
and planning can empower students to overcome challenges, stay motivated, and persist
in the face of setbacks, fostering resilience and self-efficacy.
In this phase, AI prompts should support learners by clarifying goals, anticipating
challenges, ensuring information accuracy, managing time and tasks, and identifying
necessary resources. By integrating these elements, AI can guide learners to create
actionable plans that foster motivation and support progress toward their desired
outcomes.

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Action
In the final phase of the MCM, learners transition from contemplation, goal setting and
planning to action, where they convert their aspirations and agendas into tangible
actionable steps. Actions can be conceptualized as observable behaviours, internal
cognitive and metacognitive processes, and the strategic use of knowledge or resources
developed in earlier phases. This phase requires learners to actively apply their
understanding of the task to real-world scenarios, draw on prior experiences to navigate
challenges, have confidence in their skills, and be able to employ the appropriate
resources and skills they have obtained during earlier phases. The value of teaching
learners how to act on internalized strategies to achieve their goals has been supported
in prior studies (e.g., Robillos & Bustos, 2022). We argue that by deploying these
resources, learners can execute their plans more effectively and increase the likelihood
of achieving meaningful outcomes.
Motivation for action following goal setting and planning is influenced by a multitude
of intrinsic and extrinsic factors. Extrinsic factors include receiving immediate feedback
on a completed task from the instructor, peers, or an AI chatbot (e.g., earning virtual
badges or points for solving problems, or receiving praise for making progress). And
intrinsic factors involve experiencing the satisfaction of learning something new,
feeling engaged in a stimulating conversation with ChatGPT, or finding joy in the
process of exploring and discovering information. Murayama et al. (2019) highlight the
significance of such factors in fostering motivation and enhancing learning outcomes.
AI prompts can play a key role in this phase by supporting learners to take action, set
priorities, and break complex tasks into manageable steps. These prompts can also
address emotional barriers, such as fear of failure, by building resilience and
encouraging adaptive problem-solving. By guiding learners through challenges, AI
supports them in stepping out of their comfort zones and embracing growth. Reflective
prompts further enable learners to assess their progress, celebrate milestones, and refine
strategies where needed. By integrating these prompts, AI helps learners stay focused,
maintain motivation, and effectively align their actions with their broader aspirations
and values, ensuring purposeful goal attainment.
Part IV: Practical Guidance for AI Integration
AI-Supported Strategies for Each Phase of MCM
In our exploration of the three phases of the MCM, we examined how learners can
progress through these stages with structured support. We proposed that generative AI
tools, when used as virtual companions, can help co-construct learners’ motivation by
offering personalized feedback, fostering engagement, and deepening understanding of
content. Additionally, we emphasized that AI prompts, when combined with educator
guidance, can enhance motivation for learning. Table 1 illustrates how strategically
designed AI interactions can actively build and sustain student motivation across the
different phases of the MCM. To provide further practical support, Table 2 outlines
specific types of AI prompts that educators can teach students to use in various learning
scenarios to make them engaged in each phase of the MCM.

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Table 1
Recommended prompt features and contexts for MCM
Phases

Features of prompts

AI Prompt contexts
1. Help-seeking
(a) Emotions about learning
2. Seeking clarity
(b) Perception of the task
3. Strategy request
Contemplation (c) Judgment of ability to fulfill the
4. Resource exploration
task
5. Problem-solving guidance
(d) Expectation(s) for learning
6. Skill enhancement
1. Goal clarification inquiry
Goal setting
(a) Emotional state
2. Problem-solving
&
(b) Nature of the task
3. Fact checking
planning
(c) Context of the learning
4. Time/Task management
5. Resource exploration
1. Action Initiation Assistance
2. Prioritization and action planning
(a) Concrete actions for learning
3. Task Breakdown request
Action
(b) Learning strategies analysis
4. Fear of failure support
(c) Critical evaluation of learning
5. Overcoming obstacles inquiry
6. Comfort zone challenge
7. Journey reflection
Note. This table presents an overview of the different phases of the MCM, the key features of AI
prompts, and their contextual applications that educators can use to guide students in interacting
with generative AI systems.

AI Prompts and Theoretical Alignment
To effectively support student motivation through the Motivation Construction Model
(MCM), it is essential for instructors to teach students how to craft and use AI prompts
strategically at each phase of the model. Prompts, defined as statements, questions, or
instructions input into generative AI tools, play a central role in determining the
relevance and quality of AI-generated output. As Kulkarni and Tupsakhare (2024)
argue, prompt engineering is a critical skill in natural language processing (NLP), where
well-designed inputs lead to more accurate and useful outputs. Similarly, Liu et al.
(2024) emphasize that thoughtful prompts can enhance AI performance and increase the
precision of generated content.
Incorporating prompt instruction into the MCM framework not only supports
motivation but also encourages the ethical use of AI in education. As Cain (2024) notes,
appropriate prompts can help reduce algorithmic bias, promote fairness, and align AI
use with students’ learning goals and institutional values. By equipping students with
this skill, instructors can foster both effective learning and responsible technology use.
To demonstrate how the MCM can be applied in educational practice, we present a set
of hypothetical case scenarios that illustrate the use of generative AI tools at each phase
of the model. These examples are intended to show the MCM’s potential for guiding
instructional design and enhancing learner engagement. However, as these scenarios are
conceptual in nature, we acknowledge the need for future empirical research to evaluate
the model’s effectiveness in real-world classroom environments.

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Table 2
Practical guidelines: AI prompts and their theoretical alignment at various phases of
MCM
MCM Phase

Learning
Example Prompts to GAI tools
Context
Contemplation Help-seeking Can you help me brainstorm
ideas for my upcoming research
project

Theoretical Alignment

SDT and EVT. This prompt aligns with SDT as
it reflects the learner’s autonomy in seeking
assistance for their learning. It also resonates
with EVT, as the learner’s request indicates a
belief in their ability to generate ideas with
support and a recognition of the value of
brainstorming for knowledge construction.
Contemplation Seeking
I’m feeling stuck on a concept in EVT. This prompt reflects expectancy by
Clarity
my textbook. Can [GAI tool]
seeking clarification and assistance in
explain it to me in simpler
understanding a specific concept.
terms?”
Contemplation Strategy
I’ve been feeling stressed lately. EVT. This prompt reflects expectancy by
Request
Can [GAI tool] provide some
seeking strategies to alleviate stress and
tips for relaxation and stress
improve well-being.
management
Contemplation Resource
Can you recommend some
EVT and SDT. This prompt demonstrates value
Exploration books or articles related to a
by encouraging students to seek additional
topic I’m passionate about, such resources to explore deeper into a topic of
as climate change, renewable
personal interest. It also reflects the learner’s
energy, or conservation?
enthusiasm for learning and desire for mastery
Contemplation ProblemI’m struggling to understand a SDT, EVT, and MT. This prompt illustrates the
Solving
complex math problem. Can
learner’s intrinsic motivation and autonomy,
Guidance
[GAI tool] walk with me
consistent with SDT, while also revealing their
through the steps to solve it
perception of the task’s value and their
expectancy of success, in line with EVT.
Furthermore, their proactive approach to
seeking guidance highlights an orientation
toward mastery and competence development,
as outlined in MT
Contemplation Skills
I’m interested in improving my EVT and MT. This prompt aligns with the
Enhancement writing skills. Can [GAI tool]
concept of the EVT and MT, where learners
provide feedback on a short
believe that their abilities can be developed
essay I’ve written
through practice and learning. By seeking
feedback, the learner demonstrates a proactive
approach to skill development, showing their
commitment to continuous improvement and
their willingness to learn from others’
perspectives and expertise.
Goal Setting Goal
Can you help me set a specific SDT. This prompt focuses on the student’s
& Planning
Clarification goal for my upcoming
desire for autonomy and intrinsic motivation in
Inquiry
psychology exam? I feel like I setting their own learning goals and plans.
have too much to study and too
little time to plan anything
Goal Setting ProblemI am in a hurry! My teacher
EVT and SDT. This prompt underscores the
& Planning
Solving
asked us to estimate how much student’s desire for guidance in crafting a
garbage a household produces in strategic plan to reach their goals, highlighting
a year as a group project with
the significance of expectancies (confidence in
Emily and Sam. I’m not sure
one’s ability to succeed within a limited
how to start this
timeframe) and values (the importance
attributed to achieving the desired goal).
Additionally, it aligns with SDT by

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Hajian, Chang, Wang & Lin

Goal Setting
& Planning

Goal Setting
& Planning

Goal Setting
& Planning

Action

Action

Action

Action

615

demonstrating the learner’s commitment to
success in a group project through relatedness
and collaboration
Fact
I’m feeling a bit overwhelmed MT. This prompt addresses the student’s
Checking
by my upcoming science project mindset by encouraging them to embrace a
about photosynthesis. I don’t
growth mindset, perceiving challenges as
think I can get a good grade.
opportunities for learning and growth rather
There are so many smart
than as obstacles.
students in my class
Time and
My essay is due next week. I
EVT. This prompt reflects the student’s need
Task
need to write a 500-word essay for guidance in creating a written piece in a
Management analyzing Hamlet’s uncle. I am short time frame, emphasizing the importance
stressed and I feel like Hamlet’s of expectancies (belief in one’s ability to
uncle is bad because he wants to succeed in a short period of time).
kill Hamlet. I only have 7 days
to finish this essay. How do I do
this? Help!
Resource
Can you give me some websites EVT. This prompt demonstrates that the learner
Exploration with a summary of Hamlet? I
believes that accessing summaries will
have not read the works by
contribute to their success in essay writing. By
Shakespeare. How should I
seeking guidance on how to effectively use
make use of these websites to
these resources, they indicate both their
write a 500-word essay
expectations for assistance and the perceived
value of the task.
Action
I need to write an essay for my EVT and SDT. This prompt reflects the
Initiation
science class. I’ve been
student’s need for guidance in initiating action
Assistance
researching ways to reduce my towards their goal. It emphasizes the importance
carbon footprint, but I’m
of expectancies (belief in one’s ability to
struggling to get started. Can
succeed) and values (the importance attached to
you provide some guidance?
achieving the goal) in motivating behaviour.
Additionally, the student’s acceptance of
struggling to get started reflects their need for
competence, as they seek to develop the
necessary skills and knowledge to complete the
assignment effectively (SDT)
Prioritization Here’s my plan for studying for SDT. This prompt focuses on the student’s need
and Action my final exam on Shakespeare: for autonomy and competence in initiating
Planning
read Hamlet, remember words, action towards their goal. It emphasizes the
memorize Hamlet plots, and
importance of self-directed behaviour, effective
understand the play. I want to do time management, and effective planning to
well on the exam. Can you help enhance motivation and performance.
me prioritize my studying plan
and maybe tell me what my
action plan should be?
Task
I’m planning to get an A on my EVT and MT. This prompt addresses the
Breakdown upcoming circle geometry
student’s belief in their own capabilities (selfRequest
chapter test. Although I
efficacy) and seeks strategies to increase
understand all the concepts and confidence in their ability to take action towards
theories, I still cannot memorize their goal. It emphasizes the importance of
many of them. I have spent two breaking down tasks into manageable steps to
days on the chapter memorizing enhance self-efficacy and motivation
lots of theorems but still find it
hard to remember. Can you help
me in breaking down my
studying plan into smaller, more
manageable actions?
Fear of
I’m a college student who’s
MT. This prompt focuses on the student’s

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Failure
Support

Action

Action

Action

super passionate about the
mindset by addressing their fear of failure and
environment. I’ve been
seeking strategies to adopt a growth mindset. It
dreaming of starting a recycling emphasizes the importance of viewing
program in my neighbourhood challenges as opportunities for learning and
to make a positive impact on the development, rather than as obstacles.
planet. But here’s the thing: I’ve
tried to start projects before, and
they didn’t always go as
planned. I’m worried I’ll fail
again and disappoint everyone,
including myself. Can you help
me figure out where to start and
how to overcome this fear of
failing again?
Overcoming I keep telling myself I’ll join the SDT and EVT. This prompt aligns with SDT as
Obstacles
debate team in my school, but I it reflects the learner’s autonomy in seeking
Inquiry
always back out at the last
assistance and perseverance despite setbacks. It
minute. I’ve planned to do it so also aligns with EVT by addressing the
many times, but I just haven’t
student’s belief in their ability to join the debate
been able to make it happen.
team (expectancy) and the perceived value of
Any tips to help me actually go participating in the activity
through with it this time?
Comfort
I’ve always been taking notes
This prompt aligns with SDT as it shows the
Zone
during my study sessions, but I learner’s autonomy in seeking assistance to
Challenge
feel like I can learn more
enhance their study skills. It also aligns with
effective ways to record my
EVT and MT as it reflects the learner’s belief in
notes. Can you assist me in
their ability to improve and their commitment to
improving this habit to make my continuous improvement and growth in their
study skills more impactful and study habits.
useful?”
Journey
I’ve been feeling a bit lost lately. This prompt aligns with SDT by addressing the
Reflection
I keep jumping from one goal to learner’s need for autonomy, competence, and
another without really feeling
relatedness. The learner expresses a desire to
fulfilled. I don’t know what I’m take control of their goals and actions
doing wrong. Can you help me (autonomy), understand why they may be
take a step back?
struggling (competence), and seek guidance to
align their goals with their personal values and
aspirations (relatedness).

CONCLUSION
We synthesized the key principles of SDT, EVT, and MT to develop a unified
conceptual model, which we have labelled as the Motivation Construction Model
(MCM). This model not only provides a theoretical framework but also offers practical
applications for educators to better understand and address motivational challenges in
AI-driven learning environments. The example prompts offered in our study
demonstrated the various ways in which generative AI can assist learners in initiating,
constructing, and maintaining motivation toward their learning goals and efforts. We
argue that motivation for learning can be constructed collaboratively by both educators
and AI technologies. While empirical research is required to validate our model’s utility
and effectiveness, we believe that the integration of generative AI in education,
grounded in motivational learning theories, holds immense potential to enhance
motivation, learning outcomes, and overall student success. With AI’s personalized
support, learners can break down complex tasks into manageable steps, prioritize

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actions, overcome obstacles, and continuously improve their skills and engagement with
the learning environment.
LIMITATIONS
Although our MCM model has yet to undergo full empirical validation, we suggest that
student motivation should not be treated as a given state but rather as something that
must be actively cultivated through intentional guidance by educators and potentially
supported by AI-driven interventions. While SDT, EVT, and MT offer valuable insights
into the motivational aspects of learning, it is essential to acknowledge certain
limitations inherent in these frameworks. First, these theories may not fully account for
the diversity of individual differences and cultural variations in the motivation-learning
relationship. For example, the emphasis on autonomy and intrinsic motivation in SDT
may not universally align with cultural contexts that prioritize collective goals (Hagger
et al., 2014). In such cultures, individuals may derive motivation from their roles and
responsibilities within their social groups, seeking approval and validation from others
rather than solely pursuing personal goals (Jiang & Gore, 2016). Similarly, EVT may
oversimplify the complexity of individual motivations, neglecting the impact of
emotional and social factors. Additionally, MT, while influential in shaping attitudes
toward challenges, may not capture the complex interplay of external influences on
mindset development. Furthermore, learners’ motivational patterns can shift over time
based on their evolving experiences and changing contexts.
One major concern regarding the incorporation of AI in education is the accuracy of its
generated content. For example, generative AI tools such as ChatGPT rely on large,
imperfect datasets, which can lead to biases and misinformation. This limitation may
pose risks, especially when students generate content using AI and depend on it for
learning (Uppal & Hajian, 2025). Plagiarism is another significant issue, as students can
use AI-generated content to bypass traditional plagiarism detection systems and
challenge academic integrity. Furthermore, the rapid development of AI tools may
outpace current regulations, leaving educators and institutions uncertain about what
constitutes fair use. This regulatory gap raises ethical concerns about how such tools
should be integrated into learning without promoting dishonesty (Alshahrani, 2023;
Grassini, 2023).
Additionally, while AI offers personalized support and feedback, it cannot fully
comprehend the complexity of human emotions and motivations. This missing element
can sometimes lead to potential misinterpretations or oversimplifications of learners’
needs and preferences. Reliance on AI assistance may also diminish students’ selfefficacy and self-regulation if they become overly dependent on external prompts and
guidance (Uppal & Hajian, 2025).
To address these limitations and further advance the theoretical contributions of the
MCM model, we suggest that future research pursue rigorous empirical validation to
evaluate the model’s structural integrity and practical applicability. One possible
approach is a multi-phase mixed methods study designed to examine the model’s
internal consistency, construct validity, and predictive utility across diverse educational
settings. Specifically, a sequential explanatory design (Creswell & Plano Clark, 2011)

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could be employed, beginning with a quantitative phase to collect and analyze
numerical data, followed by a qualitative phase to interpret and elaborate on the
quantitative findings to provide deeper contextual insight.
Conflicts of Interest
The authors declare no conflict of interest related to this study.
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