=Paper=
{{Paper
|id=Vol-3728/paper23
|storemode=property
|title=Persuasive AI Feedback: Enhancing Student Emotions and Engagement in Higher Education
|pdfUrl=https://ceur-ws.org/Vol-3728/paper23.pdf
|volume=Vol-3728
|authors=Omar Alsaiari,Nilufar Baghaei
|dblpUrl=https://dblp.org/rec/conf/persuasive/AlsaiariB24
}}
==Persuasive AI Feedback: Enhancing Student Emotions and Engagement in Higher Education==
https://ceur-ws.org/Vol-3728/paper23.pdf
Persuasive AI Feedback: Enhancing Student Emotions
and Engagement in Higher Education
Omar Alsaiari1,2 , Nilufar Baghaei1
1
School of Electrical Engineering and Computer Science, The University of Queensland ,St Lucia 4072, Australia
2
Department of Computer Science, College of Science and Arts, Najran University, Sharurah 68341, Saudi Arabia
Abstract
This study focuses on the employment of persuasive technology with artificial intelligence (AI) to
enhance student’s emotions and engagement in educational environments. By integrating persuasive
elements such as praise, personalization, reminders, and emojis into AI-driven feedback, we aim to
bridge the gap in understanding their effects on students’ emotions and engagement levels. Anchored in
the control and value theory of achievement emotions, our mixed-methods research will assess these
impacts using the Achievement Emotions Questionnaire-Short (AEQ-S) and engagement data from the
RiPPLE platform. We hypothesize that such AI feedback strategies will significantly improve students’
emotional experiences and engagement with learning tasks. This inquiry contributes to educational
technology by offering insights into designing emotionally intelligent AI feedback systems, potentially
enriching the learning experience for university students. Expected outcomes include practical guidance
for leveraging AI in creating more engaging and supportive educational settings.
Keywords
Persuasive AI, AIED, Student Engagement, Control-Value Theory, Learnersourcing
1. Introduction
The integration of Artificial Intelligence (AI) in education has opened new frontiers in
how learning experiences are designed and delivered. Among these advancements, AI-driven
feed-back mechanisms stand out for their potential to significantly enhance student
engagement and improve learning outcomes [1]. This study is situated at the intersection of
persuasive technol-ogy and AI. This approach leverages AI systems to deliver feedback that is
not only informative but also motivational, incorporating elements such as praise,
personalization, reminders, and the use of emojis (see Table 1) to enrich the learning
experience of university students. Despite the growing interest in applying AI in
educational contexts, there remains a notable gap in understanding the specific impact of
these persuasive elements on students’ emotions and level of engagement.
Addressing this gap, our research draws on the control and value theory of achievement
emotions to provide a theoretical framework for assessing how persuasive AI influences
students’ emotional responses with educational tasks. The theory posits that students’
emotions are significantly shaped by their perceptions of control over and the value of their
learning activities, suggesting that AI-driven feedback designed with persuasive elements
could positively affect these perceptions.
In: Kiemute Oyibo, Wenzhen Xu, Elena Vlahu-Gjorgievska (eds.): The Adjunct Proceedings of the 19th
International Conference on Persuasive Technology, April 10, 2024, Wollongong, Australia
EMAIL: uqoaslai@uq.edu.au (O. Alsaiari); n.baghaei@uq.edu.au (N. Baghaei)
© 2024 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
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Workshop ISSN 1613-0073
Proceedings
The aims of this study are twofold: first, to evaluate the extent to which persuasive AI can
enhance students’ emotions, and second, to increase students’ engagement with the RiPPLE
platform and the AI feedback tool. To achieve these objectives, we will employ a mixed-methods
approach, centering around the Achievement Emotions Questionnaire-Short (AEQ-S) [2] for a
nuanced measurement of emotional responses, alongside with an analysis of quantitative data
on engagement extracted from RiPPLE.
By exploring the potential of persuasive AI to foster more engaging and emotionally sup-
portive learning environments, this study aims to contribute valuable insights into the design
of AI systems tailored for educational purposes. The anticipated findings are expected to not
only advance academic knowledge in the domain of educational technology but also offer
practical guidance for educators and developers seeking to leverage AI to enhance the quality
of educational experiences. Through this research, we hope to illuminate pathways toward
more effective, engaging, and emotionally resonant educational technologies.
Study Hypotheses:
H1: Students receiving generative AI (GPT-3.5) feedback on the quality of their work that
includes persuasive elements(such as praise,reminder, and visual aids like emojis) will
report higher levels of positive learning-related emotions (e.g., joy, pride) compared to
students receiving generative AI (GPT-3.5) feedback that solely focuses on the qualitative
aspects of student work without employing persuasive elements.
H2: Students receiving generative AI (GPT-3.5) feedback that strictly addresses the quality of
student work will report higher levels of negative learning-related emotions (e.g., anger,
anxiety) compared to students receiving generative AI (GPT-3.5) feedback enhanced with
persuasive elements.
H3: The incorporation of persuasive technology elements in AI feedback (such as praise,
reminder, and visual aids like emojis) will significantly enhance university students’
engagement with RiPPLE and AI feedback tool.
H4: AI systems that include persuasive elements will be more effective in influencing students
to adopt the feedback and suggestions provided, leading to improved learning outcomes.
2. Background and Related Work
The fusion of technology and education has rapidly evolved, introducing persuasive technology
and artificial intelligence (AI) to enhance learning experiences. Persuasive technology in
education, through tools and strategies like persuasive SMS and web 2.0 applications, has shown
to positively impact students’ learning attitudes and engagement, with studies by Behringer et
al.[3], Goh et al.[4], Filippou et al.[5], and Widyasari et al.[6] underscoring its effectiveness.
Similarly, the domain of Artificial Intelligence in Education (AIEd) has expanded, with AI
technologies including personalized reminders and automated feedback becoming increasingly
prevalent. Despite its growth, the full pedagogical potential of AIEd and its implications for
student agency and self-regulation are not yet fully understood [7, 8]. Recent advancements
in AI-generated feedback have demonstrated its utility in enhancing student learning out-
comes, with research showing its comparability or superiority to human feedback [9, 10, 11].
Nonetheless, concerns about students’ over-reliance on AI for learning support have been raised,
highlighting the need for balanced integration [8].
Emotional persuasion within educational settings has also garnered attention, emphasizing
the significant role of emotional and rational persuasion strategies in shaping students’ learning
experiences and outcomes. The effectiveness of technology-mediated persuasion, such as
ClassDojo, in fostering social-emotional learning, along with the impact of educators’ emotional
displays on student attitudes, has been highlighted by Williamson (2017), Van Kleef et al.[12],
and others [13, 14]. These studies collectively advocate for the strategic use of persuasive
techniques to enhance students’ emotional engagement and overall educational achievement.
This background underscores the critical intersection of persuasive technology, AI, and
emotional persuasion in education, setting the stage for our investigation into how these
elements can be cohesively integrated to optimize learning environments.
Table 1
AI Feedback Persuasion Techniques
Technique Example in Writing Feedback
Personalization Feedback based on each student’s contributions and moderating style
[15]
Social Role & Ex- Designating an AI as a tutor, portraying it as a specialist in a particular field
pertise [15]
Emojis[16, 17, 18] Using emojis as a form of nonverbal and visual persuasion
Reminder [15] When providing feedback, students are also reminded to complete any out-
standing tasks
Praise [15, 19] Acknowledging and valuing students’ hard work and contributions with ap-
preciation and positive affirmation
3. Research Methodology
3.1. Introduction to RiPPLE
RiPPLE, an adaptive educational system, is based on learnersourcing and is designed to involve
students in creating various learning resources, such as multiple-choice questions (MCQs)
[20, 21]. This process encompasses drafting question content, tagging relevant topics, generating
potential answers, selecting the correct one, and formulating an explanatory rationale. Beyond
MCQs, RiPPLE allows for the creation of worked examples and general notes, integrating diverse
elements like text, images and videos. A significant feature of RiPPLE is its commitment to
high-quality student-generated content. The system employs several moderation methods,
including staff and peer reviews, and has recently integrated AI feedback. This feedback is used
both before a resource is submitted and during the peer moderation process. Once resources are
vetted and added to a course’s repository, they become available for others in the course to use,
attempt, and provide feedback on. Moreover, users can rate and comment on these resources,
fostering a collaborative and interactive learning environment within the platform.
3.2. Methodology
This study will employ a mixed-methods approach, integrating both quantitative and qualitative
analyses to investigate the emotional and engagement responses of university students to AI
feedback within a learnersourcing environment. At the heart of our methodological framework
will be the use of the Achievement Emotions Questionnaire-Short (AEQ-S) [2], designed to
measure a wide array of emotions, including enjoyment, hope, pride, anger, anxiety, and shame.
Participants will be asked to rate their emotional experiences on a scale from 1 (strongly
disagree) to 5 (strongly agree), in alignment with AEQ-S’s aim to comprehensively assess
academic emotions.
Additionally, the study will incorporate an assessment of student engagement, drawing
inspiration from the findings of Kay (2011) [22] on the impact of web-based learning tools. To
quantitatively evaluate user engagement, we will analyze data derived from interactions on
the RiPPLE platform. This data analysis is intended to provide insights into the behaviors and
interactions of users within the platform, offering a valuable perspective on the influence of AI
feedback on student engagement.
By adopting this mixed-methods approach, we aim to gain a detailed understanding of how
persuasive AI feedback affects students’ emotional states and their engagement with educational
content. The findings from this study are expected to contribute to the enhancement of AI-driven
educational tools.
3.3. Participants and Groups
This study, approved by the Human Ethics Committee at the University of Queensland, will
involve participants divided into two groups, each interacting with the RiPPLE platform in
distinct ways:
• Control Group: This group will interact with the RiPPLE platform receiving GenAI (GPT-
3.5) feedback that strictly addresses the quality of student work without any additional
persuasive elements. This will serve as a baseline for comparison against the experimental
group to evaluate the impact of standard feedback on student emotions and engagement.
• Experimental Group: Participants in this group will receive GenAI (GPT-3.5) feedback
that includes persuasive elements, such as praise, reminders, and visual aids like emojis.
This is aimed at evaluating whether the inclusion of such elements can enhance positive
learning-related emotions and increase engagement with the RiPPLE platform and AI
feedback too.
3.4. Procedures
The study will follow a structured process to ensure comprehensive data collection and analysis:
1. Participant Selection and Group Allocation: Students enrolled in a web design course
will be randomly assigned to either the experimental or control group within the RiPPLE
platform, to ensure an even distribution of participants.
2. Initial Engagement and Training Session: All participants will attend a lab session
designed to introduce them to the functionalities of RiPPLE, which will include a detailed
tutorial and a hands-on practical exercise.
3. First Questionnaire Administration: At the conclusion of round 1, a questionnaire
will be administered to gauge initial emotional responses and engagement levels.
4. Long-term Assessment: To evaluate the enduring impact of persuasive AI feedback, a
final questionnaire will be distributed at the end of the semester.
4. Future Work
Our completed pilot study, soon to be published, involved 521 undergraduate and graduate
students for a 13-week course in ”Introduction to Web Design.” The study provided key insights
into student engagement and emotional responses, such as joy, yet noted that engagement
predominantly adhered to the minimum requirements for grade contributions. Reflecting on
these outcomes, our future endeavors will pivot towards a nuanced examination of the impact
of visual versus textual persuasive elements within educational feedback mechanisms. We aim
to dissect and compare the efficacy of these differing persuasive approaches in elevating student
engagement and emotional reactions. This critical analysis is designed to uncover strategies
that not only encourage students to exceed baseline engagement levels identified in the pilot
study but also deepen their connection with the educational material.
5. Conclusion
As we prepare to embark on this research, our goal is to explore the influence of persuasive AI
elements, such as personalized feedback, social role and expertise cues, and the integration of
emojis, on the emotional responses and engagement levels of students in higher education. The
study, underpinned by the control and value theory of achievement emotions, seeks to not only
interrogate these dynamics but also to frame a comprehensive theoretical understanding of the
effects of persuasive AI in educational settings. While we are yet to collect empirical data, the
anticipated insights aim to inform the design of AI feedback systems that are more attuned to
the emotional and cognitive needs of students. This exploratory phase is crucial for setting the
stage for future empirical research, enhancing our grasp of how AI can be optimized to foster
more effective and emotionally engaging learning environments. We are eager to undertake this
investigation and contribute to the broader discourse on employing AI to elevate the quality of
educational experiences.
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