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Emerging Perspectives 
ep.journalhosting.ucalgary.ca 
 
 
 

 
How can Generative Artificial Intelligence (AI) be Effectively Incorporated into 

Middle School Art Students to Develop Creative Thinking and Enhance A Sense of 
Community? 

 
Joyce Jakobsen* 

University of Calgary 

As a middle school educator, I engage students by enhancing creativity through 
visual expression, weaving colorful threads of ideas into the cultural tapestry of 
education using emerging technologies like generative Artificial Intelligence (AI). 
Research highlights that post-pandemic challenges center around connection and 
belonging. Expressing feelings through writing and art fosters connection, boosts 
creative confidence, and empowers self-efficacy. This study aims to identify emerging 
technologies that enhance creativity and revitalize instruction, inspiring student 
learning. This research uses qualitative content analysis to evaluate curriculum 
content, and looks at AI as a tool to scaffold knowledge based on Vygotsky’s theory 
on Zone of Proximal Development (ZPD). The research targets art and language 
educators who utilize generative AI to help students express their thoughts through 
creative disposition, even if English is not their first language. Three issues within 
current educational practices and how AI might be used as a tool to help include the 
following: 1) Hesitation to begin work: students often hesitate to express their ideas 
due to perceived limitations. AI can help by providing tools for creative expression 
on platforms for visualizing their thoughts. 2) Readiness to learn: learning can be 
intimidating; and there is a perceived learning curve to make art and motivate 
language learning. AI can introduce novelty and engagement through collaborative 
AI-generated collaborative projects to make learning more enjoyable and accessible. 
3) Increased student needs: Personalized learning environments are important. AI 
can adapt to individual student needs, offering insights to support diverse learners 
and paces. AI serves as a mediation tool to enhance learning efficiency in both 
language and artistic acquisition. It benefits English as a Second Language (ESL) 
students by facilitating prompt engineering, evaluating understanding, and fostering 
critical thinking. Uncovering the connection between prompts, creative thinking, and 
self-expression could help guide educators and AI developers in designing more 
effective teaching tools and pedagogy. 

Key words: Art education, creativity, generative Artificial Intelligence (AI), English as a Second 
Language (ESL), prompt engineering 

Jakobsen, J. (2025). How can generative artificial intelligence (AI) be effectively 
incorporated into middle school art students to develop creative thinking and enhance a 
sense of community? Emerging Perspectives. 

*joyce.jakobsen@ucalgary.ca OR joyce.jakobsen@alumni.ucalgary.ca 

http://ep.journalhosting.ucalgary.ca
mailto:joyce.jakobsen@ucalgary.ca


Jakobsen / Emerging Perspectives (2025)                                 2 

Introduction 
 

The impact of emerging technologies on education is pivotal in addressing the challenges 
posed by the pandemic, particularly in fostering connection and creativity. Integrating emerging 
technology can support students’ development of personal skills within the “learning process 
seems to affect creativity positively” (Li et al., 2022, p. 7). Robinson (2020) detailed challenges 
encountered post-COVID, which included the normalization of stress, anxiety, and behavioural 
patterns. Success for students is defined as a state of fulfilment, where “individuals are 
recognized, and diversity of talent is celebrated” (p. 8). AI tools can help student learning by 
creating a collaborative space for self-expression and problem-solving by developing 
communication. Robinson (2020) goes on to say that “they boost creative confidence, 
self-efficacy, and a sense of belonging. Emerging technologies revitalize instruction, inspire 
learning, and foster connection, crucial for students’ social and emotional well-being 
post-pandemic” (p. 8).   

The research uses qualitative content analysis to evaluate curriculum content, and explores 
AI as a tool for constructivist knowledge scaffolding based on Zone of Proximal Development 
(Vygotsky, 1978). This aligns with knowledge scaffolding, where support is gradually reduced as 
students become more capable. AI helps: 1) Enhance language learning efficacy for ESL 
students (Wei, 2023), 2) mediate learning though prompt engineering (Walter, 2024), and 3) 
improve evaluative judgement to check for understanding and critical thinking (Temirgalieva, 
2025). Belshaw (2014) says, “the way to learn hard skills is constant practice within a relevant 
context” (p. 34). Three issues in current education are: 1) Hesitation to start – students have but 
perceive inability to proceed. 2) Readiness to learn – there is a “learning curve” to make art, but 
meaningful goals help motivation. Research implies that an intrinsically motivated disposition 
has a highly positive effect on increasing creative potential and output (Amabile & Pratt, 2016, 
as cited in Kelly, 2020). 3) Increased student needs – 86% of teachers say social challenges 
among students have increased (Alberta Teachers’ Association [ATA], 2021). These issues are 
prevalent in K-12 education, especially for middle school students negotiating their sense of 
identity and belonging. Technology helps navigate learning: 1) Creative expression – AI supports 
creative expression with prompts, reducing learning curves. 2) Novelty – AI engages students in 
creating and critiquing. The potential to foster creative engagement in design and collaboration 
further empowers students to generate innovative solutions (Bahroun et al., 2023, p. 7). 3) 
Personalized learning – assuming equitable access, AI can support divergent learning paths and 
paces.  

Methods 
 

The study adopts qualitative content analysis to evaluate curriculum content and explore 
how AI tools can mediate learning and scaffold student knowledge in the form of a "more 
capable other" (Vygotsky, 1978). The tools analyzed include Adobe Firefly, Canva, and 
Microsoft Co-Pilot – selected for their accessibility and widespread classroom use. AI acts as a 
coach (Mollick & Mollick,, 2023) where its role is prompt metacognition that creates 
opportunities for reflection and can improve learning outcomes. To optimize interaction with the 
AI Coach, Mollick suggests to “share challenges with the AI Coach and ask directly for advice, 
give it context, and ask questions and seek clarification.” (p. 22). Engaging with the group 
through generative AI allows educators to use AI as a mentoring tool, where the educator can 
mentor student ideation. By refining prompts iteratively, the study assesses the impact of 

 



Jakobsen / Emerging Perspectives (2025)                                 3 

AI-generated art on students’ creative confidence and language development, with teachers 
guiding and connecting concepts.  

Data Collection 

Art generated by three AI tools was analyzed. Table 1.1 (Appendix A) guides teachers in: 
(1) Transforming lesson plans and refining prompts; (2) Demonstrating tool usability through 
image analysis; and (3) Recommending practices for art educators. Project-based learning 
encourages creative expression through art. Refining prompts through feedback loops builds 
language and conceptualization skills.  

Results  
  

A Qualitative Content Analysis (QCA) of AI-generated outputs was conducted to assess 
creativity and expression, as QCA “addresses the content of texts, whether the texts are books, 
images, physical artifacts” (Drisko & Maschi, 2015, p. 85). Table 1.2 (Appendix B) shows how 
each prompt was coded based on theme, creativity, and technique. Prompts were modified 
through review and reflection. Table 1.3 (Appendix C) outlines the coding process: (1) Initial 
reaction—Did the image inspire? Match the prompt? Could it evolve? (2) Modified 
coding—refined prompts for alignment. Canva outputs required refinement when “Canadian 
identity” appeared too broad. CoPilot’s Salvador Dali-style art with melting maple syrup 
symbols was an attempt at combining identity and integrity. Images that leaned on stereotypes 
were marked for revision.  

Data Analysis 

This comparative analysis revealed distinct styles from each AI, requiring prompt 
adjustments to clarify artistic direction. Table 1.5 (Appendix B) reflects on how outputs inspired 
ideation, feedback, and iteration. AI can help refine prompts and language use, enhancing 
creative expression. Teachers and students benefit as language develops naturally while 
searching for specific keywords.  

Significance of Research 

AI tools fulfills the role as a coach that supports Vygotsky’s ZPD that builds knowledge 
gaps through creative expression. Students often spend time visualizing ideas or searching 
online. Generative AI may act as a “creative partner” (McCormack et al., 2019), but sometimes 
lacks cultural depth. Canva and Firefly produced less identity-rich outputs, reinforcing this 
limitation. Table 1.5 (Appendix D) connects findings to theory, suggesting that generative AI 
functions best when framed not as a replacement for human creativity, but as a scaffold—a tool 
that supports ideation, sparks dialogue, and encourages reflection (Sawyer, 2012). AI can serve 
as a tool to support learners envision what they may not see independently. It supports critical 
and deep thinking. Intentional prompt design and ongoing reflection can become an integral part 
of education practice. Positive feedback and early success help build belonging. AI also 
contributes to community and collaboration among students. 

Implications 

 



Jakobsen / Emerging Perspectives (2025)                                 4 

Bates (2023) reminds us that teaching is a human activity, not merely technical. They must 
always be a human element in learning. Eaton (2023) raises concerns that students who bypass 
critical thinking and creativity may struggle to apply knowledge meaningfully, emphasizing the 
need to prioritize creative practice over memorization and standardized responses. Students may 
misuse AI or submit AI-generated work as their own. To mitigate this, schools can set specific 
guidelines and age-appropriate frameworks for AI use. Ethical concerns in summative 
assessments are valid and may limit learning. AI can be a tool used to augment human creativity 
(Ivcevic & Grandinetti., 2024) and nurture human imagination. Seventy-one percent of Alberta 
teachers believe educational technology enhances inquiry-based learning (ATA, 2021). Training 
for educators can include AI integration into lesson design.  

Conclusion  
 

This study explores how generative AI promotes creativity and belonging. AI acts as a 
coach for students and teachers. Despite increasing AI integration, educators continue to actively 
monitor and guide learning. As Belshaw (2014) suggests “skills are not learned in isolation” (p. 
33) as learning is not insular. Content analysis of prompt feedback supports ongoing refinement 
and helps non-native speakers express their ideas clearly. AI enhances imagination and supports 
ideation —not just outcomes. Each tool offered new creative methods and inspired new forms of 
artmaking. This research highlights AI's potential to spark creativity. Key findings include: 1. 
Generative AI boosts creative idea generation and learning in both language and art. 2. AI 
mediates learning through prompt engineering. 3. AI inspires learning, reflection, and critical 
thinking.  

Recommendations 

1. Monitor and Adapt: Adjust prompts regularly as outputs and student needs evolve. 
2. Professional Development: Train teachers to coach students to use AI ethically and 

creatively.  
3. Future research: Explore AI’s long-term impact in Art Education through pilot programs 

or case studies. Weaving the connection between prompts, creative thinking, and self-expression 
could help in designing more effective teaching tools and pedagogy. 

Acknowledgements  
  
 

I thank Dr. Soroush Sabbaghan for his guidance and patience, Dr. Harrison Campbell for 
his humor and for challenging me to reach greater heights, and the Werklund School of 
Education for their resources and opportunities. I am also grateful for my family and friends for 
their support, and to my husband, my rock, whose unwavering belief makes all things possible. 

 



Jakobsen / Emerging Perspectives (2025)                                 5 

References  
  
Alberta Teachers’ Association (2021). Growing up digital (GUD) Alberta project, 

https://legacy.teachers.ab.ca/SiteCollectionDocuments/ATA/About/Education%20Resear 
ch/Promise%20and%20Peril/COOR-101-10%20GUD%20Infographic.pdf  

Bahroun, Z., Anane, C., Ahmed, V., & Zacca, A. (2023). Transforming education: A 
comprehensive review of generative artificial intelligence in educational settings through 
bibliometric and content analysis. Sustainability (Basel, Switzerland), 15(17), Article 
12983. https://doi.org/10.3390/su151712983  

Bates, A. W. (2023, July 22). Education & cognition public lecture with Dr. Tony Bates 
[Video]. Youtube. https://www.youtube.com/watch?v=MpWJs7z9nPI 

Belshaw, D. (2014). The essential elements of digital literacies. http://digitalliteraci.es   

Drisko, J. W., & Maschi, T. (2015). Content analysis. Oxford University Press. 

Eaton, S. (2023, March 15). Academic integrity and artificial intelligence: Implications for 
plagiarism and academic writing [Video]. 
Youtube. https://www.youtube.com/watch?v=9QNNPVSC24w 

Ivcevic, Z., & Grandinetti, M. (2024). Artificial intelligence as a tool for creativity. Journal of 
Creativity, 34(2), Article 100079. https://doi.org/10.1016/j.joc.2024.100079 

Kelly, R. (2020). Collaborative creativity: Educating for creative development, innovation, and 
entrepreneurship. Brush Education. 
https://ezproxy.lib.ucalgary.ca/login?url=https://search.ebscohost.com/login.aspx?direct= 
true&db=nlebk&AN=2394555&site=ehost-live  

Li, Y., Kim, M., & Palkar, J. (2022). Using emerging technologies to promote creativity in 
education: A systematic review. International Journal of Educational Research Open, 3, 
Article 100177.  https://doi.org/10.1016/j.ijedro.2022.100177 

Mollick, E., & Mollick, L. (2023). Assigning AI: seven approaches for students, with prompts. 
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Robinson, K. (2020). A global reset of education. Prospects, 49, 7-9. 
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Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. 
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Walter, Y. (2024). Embracing the future of artificial intelligence in the classroom: The relevance 
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https://doi.org/10.1186/s41239-024-00448-3 

 



Jakobsen / Emerging Perspectives (2025)                                 6 

Wei, L. (2023). Artificial intelligence in language instruction: Impact on English learning 
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McCormack, J., Gifford, T., & Hutchings, P. (2019). Autonomy, authenticity, authorship, and 
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https://doi.org/10.1080/14626268.2019.1600769


Jakobsen / Emerging Perspectives (2025)                                 7 

Appendix A 

 
Table 1.1 Sample Lesson Plan 

Objective Encourage creativity using AI for idea development. 
Introductio
n  Review Elements of Art, discuss identity in art, and introduce three AI tools 

Procedure  Students think-pair-share ideas combining art elements are identity themes. Refine 
prompt with AI (e.g., “Show me textural art that represents specific identity”).  

Conclusion Students begin artwork; teacher supports and celebrates progress.  
 

 



Jakobsen / Emerging Perspectives (2025)                                 8 

Appendix B 
 

Table 1.2 Prompt Categories and Tools Used in Creative Exploration of Identity through AI 
Category  Description # of prompts created 
Artist-Based Prompts Prompts in specific artists’ styles (e.g., 

Picasso, Matisse, Dali, Warhol)   
14 

Technique-Based 
Prompts 

Mixed-media collage, watercolor, pencil 
shading, perspective 

14 

Style-Based Prompts Barbie, Graffiti, 8-bit pixel art, pop art 6 
Theme-based Prompts  Self-portrait, Identity themes (e.g., 

Canadian)  
28 total  

 

 



Jakobsen / Emerging Perspectives (2025)                                 9 

Appendix C 
 

Table 1.3 Criteria and Explanation of Prompt Outputs 
Criteria Explanation  
Inspiration  Did the image inspire new ideas to create?  
Clarity Did the output articulate the category? 
Evolve How can the idea grow or develop further? 

 
Table 1.4 Insights 

 

 
 

 

Table 1.4 Insights (continued) 

 



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Appendix D 
 

Table 1.5 Summary of findings and theoretical connections 
Findings Theoretical Connection 
Specific prompts inspired deeper engagement Constructivist theory: building knowledge  
Vague prompts led to generic outputs AI reflects limitations of training data  
Refinement improves outcomes  ZPD Scaffolding theory (Vygotsky, 1978) 
User experience depended on knowledge 
level 

Creative development theory (Sawyer, 2012)  

 

 


