Using ChatGPT to Support Instructional Design: Student Instructional Designers’ Perceptions and Experiences in Graduate Courses

Introduction

Artificial intelligence (AI) has long been a subject of academic and professional interest, with applications spanning learning analytics, adaptive learning systems, and intelligent tutoring (Zawacki-Richter et al., 2019). More recently, the emergence of generative AI (GenAI) tools such as ChatGPT has introduced new opportunities and challenges for teaching and learning. GenAI tools have demonstrated the capacity to support learning activities by generating examples, explanations, and assessments aligned with instructional outcomes, thereby enabling more personalized learning experiences (Bahroun et al., 2023; Office of Educational Technology, 2023). At the same time, concerns have been raised regarding ethical use, academic integrity, and the need for AI literacy among both students and educators (Kasneci et al., 2023; Luo et al., 2025; Rudolph et al., 2023).

Despite their disruptive potential, the application of GenAI in instructional design, specifically from novice instructional designers’ perspectives, remains underexplored. Instructional design plays a critical role in shaping the quality of teaching and learning by connecting learning objectives, instructional strategies, activities, and assessments (Morrison et al., 2019). Recent scholarship has examined the role of GenAI in education broadly (Kasneci et al., 2023; Rudolph et al., 2023) and has begun to identify potential uses for supporting course development (Chng, 2023; Parsons & Curry, 2024). However, few studies have specifically investigated how student instructional designers, often novices entering the field, experience the integration of GenAI in their design processes. Given that instructional designers’ work directly influences instructional quality and learner outcomes, understanding novice designers’ perspectives on using ChatGPT is both timely and important.

Recognizing this gap, the purpose of this study is to explore the experiences and perspectives of graduate student instructional designers in using ChatGPT to support their instructional design processes. By examining how novice designers perceive the affordances and limitations of ChatGPT, this study contributes to emerging discussions on the role of GenAI in education. The findings provide insights for both educators and instructional design practitioners seeking to leverage generative AI in ways that enhance efficiency, effectiveness, and ethical use in instructional design practice.

Literature Review

ChatGPT in Education

ChatGPT, developed by OpenAI, is a state-of-the-art natural language processing model. This model is trained on extensive datasets, including books, news articles, websites, and Wikipedia, utilizing machine learning algorithms to recognize and generate coherent and contextually relevant text (Gimpel et al., 2023). This extensive training allows ChatGPT to perform a variety of natural language tasks, such as computer programming and human-like text generation, responding effectively to both simple and complex prompts (Cooper, 2023; Kasneci et al., 2023). With over 100 million users in just two months, ChatGPT has become the fastest-growing application in history (Bartz, 2023).

The rise of ChatGPT highlights its significant implications for various sectors, particularly education. The discourse surrounding AI in education is extensive, noting both potential benefits and concerns (Atlas, 2023; Baidoo-Anu & Ansah, 2023; Gimpel et al., 2023; Rudolph et al., 2023). Issues like plagiarism and cheating are frequently discussed (Atlas, 2023; Gimpel et al., 2023; Office of Educational Technology, 2023; Rudolph et al., 2023). Nonetheless, substantial evidence supports the benefits of AI in education. For instance, Luckin and Cukurova (2019) noted the increasing demonstration of AI’s potential in education, suggesting that its full impact has yet to be realized (Celik, 2023; Luckin et al., 2022). Moreover, GenAI, such as ChatGPT, is used in diverse educational contexts, including automated essay scoring, personalized tutoring, classroom assistance, language translation, and enhancing writing, research, and communication skills (Atlas, 2023). Additionally, it assists in creating syllabi, quizzes, exams, summaries, reports, and various research documents while facilitating email and chatbot communication, organizing meetings and events, conducting campus tours, providing policy guidance, and generating reports (Atlas, 2023).

Empirical studies indicate that ChatGPT can significantly enhance productivity and learning outcomes (Farrokhnia et al., 2023; Kasneci et al., 2023) by promoting independent learning through personalized and effective learning experiences (Qadir, 2022; Zhai, 2022). Kasneci et al. (2023) highlighted ChatGPT’s ability to provide accurate explanations and systematically guide learners through problem-solving processes, thereby enhancing personalized learning experiences.

ChatGPT for Instructional Design

The exploration of GenAI in education has gained significant attention. Research indicates that ChatGPT holds the potential to generate instructional content and provide personalized learning experiences for students (Farrokhnia et al., 2023; Lee, 2023; Tlili et al., 2023). Specifically, in instructional design, ChatGPT can collaborate with instructional designers or educators to enhance content creation, instructional strategy delivery, and assessment processes (Choi et al., 2024). Several scholars have begun to investigate how ChatGPT might be systematically integrated into instructional design practice (Chng, 2023; Choi et al., 2024; Karaman, 2024). For example, Chng (2023) proposed how the ADDIE model and instructional design practices could be reimagined through AI integration, incorporating both assisted and autonomous forms of intelligence. Likewise, Choi et al. (2024) conducted a SWOT analysis and identified strengths such as generating learning objectives, providing material suggestions, and producing discussion prompts, instructional activities, and assignments. However, their analysis also highlighted weaknesses, including a lack of contextual understanding, low reliability of resources, repetitive or vague instructional suggestions, shallow assessment design, and output inconsistencies.

In addition to conceptual work, empirical studies have examined ChatGPT’s application in instructional design, revealing both advantages and limitations. The advantages include idea generation (Davis & Lee, 2023; Kozan et al., 2025; Luo et al., 2025), content development, writing improvement, information search, and assessment design (Karaman, 2024; Yang & Stefaniak, 2025), as well as streamlining elements of the design process (Luo et al., 2025), improving efficiency (DaCosta & Kinsell, 2024; Kumar et al., 2025), and even enhancing student achievements (Karaman, 2024; Onal & Kulavuz-Onal, 2024). For instance, Kumar et al. (2025) found that instructional designers used GenAI to develop materials and activities, leading to increased efficiency. Karaman (2024) demonstrated that lesson plans developed with ChatGPT improved primary school students’ mathematics achievement. Similarly, Luo et al. (2025) identified common uses of GenAI among professional instructional designers, including idea generation, managing routine tasks, streamlining design processes, and fostering collaboration. Complementing these findings, Chai et al. (2025), in a review of 71 studies, reported that GenAI can be applied across all stages of instructional design: analysis, design, development, implementation, and evaluation.

Despite these benefits, the literature consistently highlights significant challenges. Concerns include the reliability and overall quality of AI-generated outputs (Luo et al., 2025; Yang & Stefaniak, 2025), risks to data security and privacy, and unresolved issues related to authorship, ownership, and plagiarism (Luo et al., 2025). Additional issues involve AI’s tendency to fabricate information (Davis & Lee, 2023), algorithmic bias (DaCosta & Kinsell, 2024), and limitations in domain-specific customization (McNeill et al., 2024; Parsons & Curry, 2024). For example, Luo et al. (2025) found that designers expressed concerns over reliability, security, and authorship when adopting GenAI tools. Parsons and Curry (2024) showed that while ChatGPT can complete graduate-level instructional design assignments, it struggles to adapt content to specific contexts. Similarly, Onal and Kulavuz-Onal (2024) reported that ChatGPT can generate accurate and creative assessments but should not replace human expertise or professional judgment. Together, these studies frame ChatGPT as a potentially valuable collaborative tool in instructional design, though with significant limitations.

Overall, existing research suggests that while ChatGPT offers valuable affordances for instructional design, it also raises questions about reliability, accuracy, and responsible use. Thus, educating instructional designers on how to integrate GenAI effectively is increasingly important. Yet, limited research has examined how student instructional designers, who are still developing their professional identities and skills, perceive and experience ChatGPT in instructional design contexts. Given that novices’ needs and perspectives often differ from those of expert practitioners, ChatGPT may provide unique forms of support in their learning and practice. Understanding these perspectives is essential for preparing instructional design educators to guide students in responsibly and effectively leveraging GenAI. Therefore, this study investigates the experiences and perspectives of graduate student instructional designers regarding the use of ChatGPT in instructional design. The following research questions guided the study.

1. How can ChatGPT be used for instructional design from the perspectives and experiences of student instructional designers?

2. What are the advantages and challenges of using ChatGPT for instructional design from the perspectives and experiences of student instructional designers?

Methods

This qualitative study used a phenomenological research design, which aims to understand and interpret specific aspects of shared human experiences (Moran, 2002; Smith, 1996). Unlike positivist approaches that seek definitive conclusions, phenomenology aims to provide a detailed and nuanced account of people’s perceptions and lived experiences (Smith et al., 2009). The phenomenon under investigation was defined as the ways in which students experienced and interpreted the integration of ChatGPT into their instructional design work, including perceived affordances and challenges. Phenomenology is well-suited for such inquiry because it seeks to uncover the essence of participants’ experiences and the meanings they ascribe to them (Creswell & Poth, 2016).

Context

The study context is four online graduate-level instructional design courses at a Mid-Western University in the U.S. The author served as the designer, developer, and instructor for these courses, which include: (1) Interactive Course Design (Fall 2023), (2) Mobile Learning Technologies (Fall 2023), (3) Serious Games Design (Winter 2024), and (4) User Experience Design for Learning (Winter 2024). Each course focuses on using learning technology to design and develop instructional experiences, guided by relevant theories. They all employ a problem-based learning (PBL) approach and emphasize practical, hands-on activities that require students to produce tangible learning products.

The course design was based on the principles of constructivism (Vygotsky, 1978), which emphasizes students’ active construction of knowledge through the integration of new and existing knowledge within social interactions. Based on the existing PBL literature, the author developed a new framework called 4S PBL, self-regulated learning (SRL) and socially shared regulated learning (SSRL) Strategies to support PBL, aimed at fostering self- and socially-regulated learning in online PBL settings, particularly for technology-intensive courses (Zhang & Zhu, 2023). This framework outlines strategies for supporting online PBL at different stages from SRL and SSRL perspectives (Zhang & Zhu, 2023). Four courses were designed according to general PBL principles and the 4S PBL strategies. For example, reflection activities were embedded throughout the semester, including mid-term group reflections and end-of-semester individual reflections. Each course spanned 15 weeks and was delivered asynchronously, complemented by weekly synchronous office hours. Instructional videos, lasting 5-10 minutes, were recorded by the instructor weekly. The course content was hosted on Canvas, a widely used learning management system in higher education, and was organized into weekly modules.

A typical module included weekly learning objectives and a to-do list, instructional videos, a reading list, online discussions, hallway conversations, and assignments. The weekly learning objectives and to-do list provided overarching goals and outlined the tasks students were expected to complete. Instructional videos covered the week's content knowledge, while the reading list detailed required readings. The online discussion forum served as a platform for students to discuss, reflect, and share their thoughts on the week's topic. Hallway conversations were available for students to ask questions to the instructor and teaching assistant.

These four courses have gone through several iterations. Since Fall 2023, the instructor has incorporated new elements to leverage the widely used technology, ChatGPT, to support students’ learning. Guidelines for utilizing ChatGPT to support learning were created and shared with students during the first week of the semester to facilitate their instructional design efforts. These guidelines outline 12 potential methods for students to use ChatGPT in learning design and technology courses. The detailed information was described in Zhu (2025a).

Participants

Since these four courses are elective for graduate students, those enrolled are typically interested in the use of technology for instructional design. The students are majoring in learning design and technology and are currently working as instructional designers, K-12 teachers, corporate trainers, professors in higher education, etc. Detailed demographic information of participants has been reported in Zhu (2025a). Most of them were at the novice stage of their professional development. There were 21 reflection papers from 17 participants who had full- or part-time jobs while taking the courses, with four participants enrolled in two of the courses.

Data Sources

This study employed multiple data sources, including primary data from students’ reflection papers and supplementary data from learning artifacts (i.e., mid-term group reflections and final projects). A total of 21 reflection papers, submitted as the final assignment of the semester, were designed to encourage students to critically reflect on their learning processes and experiences. Some prompts specifically addressed ChatGPT’s role in instructional design and related challenges. For example, students were asked: “How can ChatGPT support learning experience designers or instructional designers in designing learning modules or materials?” and “What are the challenges or disadvantages of using ChatGPT to support your learning in this course?”

In addition, five mid-term group reflection documents and 13 final projects were analyzed. The mid-term group reflections focused on students’ project experiences and processes, while the final projects consisted of e-learning modules or courses, mobile learning applications, and serious game prototypes or products. The integration of multiple data sources enabled triangulation, thereby enhancing the trustworthiness of the study.

Both research questions were addressed primarily through the final reflection papers, which captured students’ overall learning experiences and perspectives at the end of the semester. These were supplemented by the mid-term group reflections, which provided insights into students’ learning processes, and by the final projects, which offered concrete evidence of their design work. Together, these data sources provided a more comprehensive understanding of students’ experiences.

Data Analysis

Thematic analysis (Braun & Clarke, 2006) was used to analyze student instructional designers’ reflection papers and mid-term group reflection documents. Thematic analysis enables researchers to identify patterns within datasets to describe the studied phenomenon (Guest et al., 2012). This method comprises six distinct phases (Bernard & Ryan, 2010). First, researchers immerse themselves in the data to gain familiarity by repeatedly reading through the dataset to recognize emerging patterns. In the subsequent phase, researchers generate codes by labeling significant words, phrases, sentences, and paragraphs. Next, these related codes are grouped into overarching themes. Following this, researchers review and refine the themes, either consolidating or dividing them as necessary. In the following phase, they define and name the themes. Finally, the results are compiled and prepared for dissemination. Student final projects were reviewed as supplementary data to triangulate the primary data, reflection papers.

A codebook was developed to guide the thematic analysis and ensure consistency in the coding process. The codebook included three main components: (a) themes, (b) the codes, and (c) one illustrative example drawn directly from the data. The initial codes were derived inductively from a close reading of the reflection papers and learning artifacts. These codes were then iteratively refined through multiple rounds of analysis, during which overlapping codes were merged, and ambiguous codes were clarified. The codebook served as a living document, evolving as new insights emerged, and provided a transparent record of how raw data were organized into higher-order themes.

For research question 1, the code includes brainstorming, outlining design, content and material creation, learning activity design, assessment design, and enhancing text quality (see Table 1).

Table 1 Approaches of leveraging ChatGPT for instructional design reported by student instructional designers

For research question 2, the main theme includes the benefits and challenges of using ChatGPT for instructional design (see Table 2).

Table 2 Benefits and challenges of using ChatGPT for instructional design from student instructional designers’ perspective

Terms

In this study, the terms efficiency and effectiveness adopted definitions in instructional design research. Efficiency is generally understood as the relationship between instructional outcomes and the resources expended, such as time and effort, to achieve those outcomes (Morrison et al., 2019). By contrast, effectiveness refers to the extent to which an instructional intervention enables learners or instructors to achieve the intended learning objectives or improves instructional quality (Reigeluth & Keller, 2009).

Anchoring the analysis in these constructs, efficiency was identified through participants’ reports of reduced lesson-planning time, lowered workload, and streamlined access to instructional resources. For effectiveness, it refers to participants’ perceptions of improved instructional quality and enhanced clarity of learning materials. While some of the findings may also reflect productivity, that is, the ability to accomplish more tasks in the same or less time, in this study, productivity is situated as an indicator of instructional efficiency.

Findings

How Can ChatGPT be Used for Instructional Design from the Student Instructional Designers’ Perspective?

All student instructional designers reported how ChatGPT supports their instructional design process (see Table 1). First, ChatGPT serves as a valuable tool for brainstorming ideas in the instructional design process. Brainstorming diverse ideas helps student instructional designers creatively design instructions and make the process efficient. Sally, an education specialist, mentioned, “I think ChatGPT has the opportunity to speed up the design process by helping learning experience/instructional designers brainstorm ideas quicker and then devote more time to refinement and iteration of the idea.” Scarlett, a content development specialist in corporate settings, further explained, “ChatGPT can help brainstorm ideas. This is especially helpful for independent instructional designers who don’t have a team to brainstorm with.”

Second, ChatGPT assists student instructional designers in outlining their instructional designs at the beginning. Victoria, an annual campaign officer at a University Donor Experience Team, noted, “ChatGPT can provide outlines that designers can follow, which will assist them in their design work.” Victoria detailed her experience: “When I asked ChatGPT what designers should consider when designing learning modules, it provided a comprehensive list of key considerations with descriptions. These included best practices such as learner analysis, learning objectives, and content organization. ChatGPT provided a solid outline on which I could build.” Similarly, Tracey, a staff member in Foundation Relations at a University, stated, “I do think ChatGPT could support designers in developing a starting point and organizational structure of how information or design plans can be presented.”

Third, ChatGPT supports content and materials creation for instructional designers. With this function, instructional designers can let ChatGPT help generate the initial materials for their design. Scarlett shared her perspective, “ChatGPT can help instructional designers create content, including scenarios and examples.” Hank, an environmental health and safety specialist, added, “ChatGPT can assist in creating content for learning modules, personalize the content to the learner’s preference, design interactive exercises, and recommend relevant learning resources.” Julie, a special education teacher, noted, “I also feel that ChatGPT does an excellent job of assisting with the design of materials.”

Fourth, ChatGPT helps design engaging learning activities. Hank stated, “It is a very versatile tool that all designers can use to create engaging and effective learning modules.” Fred, a research assistant with an adolescent development research lab, also shared, “An activity could be centered around how the AI chatbot responded and the impact the responses had on the users.” Cecilia, a K-12 teacher, added, “When designing a learning experience, creators could embed hints for when and how their learners could leverage ChatGPT as support at potential pain points, much like what was done at the onset of this course. It was helpful to receive guidance on how and when to explore its usage.”

Fifth, ChatGPT helps instructional designers generate assessment questions to evaluate students’ learning. Spencer, an instructional designer in corporate settings, shared his experience, “I use it to help develop multiple-choice questions and answers. ChatGPT does a great job of providing a starting point for a set of multiple-choice questions from a given text. The answer options are not always perfect, but can be refined when added to the course. It has been a huge time saver when I needed to develop a course assessment.”

Lastly, instructional designers can use ChatGPT to improve text quality. Spencer shared, “I use it to help reduce particularly long passages I receive from stakeholders. ChatGPT does a decent job rewriting text succinctly while keeping the desired message. At worst, it gives me a better starting point to make my edits.” Similarly, Sophie, a K-12 teacher, stated, “What I appreciated about ChatGPT the most is that it compiled all the research into lists of ten and recognized the interconnectedness of the information.”

What are the Benefits and Challenges of Using ChatGPT for Instructional Design from the Student Instructional Designers’ Perspective?

Among the 17 participants, 16 student instructional designers reported the benefits and challenges of using ChatGPT in their work. The main benefits highlighted were increased efficiency and effectiveness. However, they also identified several challenges, including technology limitations, lack of AI literacy, ethical concerns, and potential negative consequences (see Table 2). The benefits of using ChatGPT reported by student instructional designers include enhancing efficiency and effectiveness. For example, Patrick, a collections manager in a library, mentioned, “The influx of AI tools such as ChatGPT has the potential to increase our productivity and efficiency.” Additionally, ChatGPT has the potential to improve the quality of the product. Jade, a trainer in corporate settings, noted, “It guided me to find platforms for enhancing my e-learning module, particularly when I needed a self-assessment quiz application.” Moreover, Camila, an instructional designer in corporate settings, shared her perceptions, “Leveraging ChatGPT's capabilities as a creative partner holds significant potential for enhancing the overall quality and efficiency of e-Learning design.”

Despite these benefits, there are challenges associated with using ChatGPT. Sixteen out of 17 participants identified various challenges. The first significant issue is technology limitations. ChatGPT can sometimes provide inaccurate information. Hank stated, “The chatbot does provide incorrect information at times, so you really need to evaluate the response to ensure it makes sense and is correct.” Jasmine, an associate professor at a University, had a similar experience: “ChatGPT often directs users to links that no longer exist and cannot read Google Docs. If asked to create a spreadsheet or Google Doc, it cannot be accessed.” Moreover, ChatGPT has limited access to resources. Patrick mentioned, “One finding is that ChatGPT does not have access to subscription-based journal articles, limiting it to open-access information on the internet.” Additionally, ChatGPT may not always provide context-specific information. Camila stated, “Ensuring that the content generated by ChatGPT accurately aligned with the specific educational goals and context of the course was a continuous challenge. While it provided a starting point, there was a need for continuous refinement and extensive fact-checking to ensure the content's relevance and coherence with the overall learning objectives.” As a result, student instructional designers often do not fully trust ChatGPT. Lia, an assistant director of a health certificate program, mentioned, “While I don’t yet trust ChatGPT to generate accurate information, I think it can be a good tool to get unstuck.” Spencer shared, “I still prefer using trusted sources for research as there is always the possibility of getting incorrect information from ChatGPT.”

The second challenge is the insufficient AI literacy among users to leverage ChatGPT effectively. Jade mentioned, “While ChatGPT provided substantial support, challenges included uncertainty about its usage.” Similarly, June wrote, “The only challenge I experienced in discovering ChatGPT was my inexperience with the platform and its uses.” Tracey shared her challenges, stating, “It takes time to learn how to use the tool effectively. If a prompt doesn’t yield the desired result, I sometimes abandon the pursuit to avoid going down a rabbit hole trying to decipher the right prompt.” Additionally, student instructional designers are not familiar with prompt engineering and often feel discouraged while using ChatGPT as they have to try it again and again to get what they want. Victoria explained, “Reformatting a question multiple times to get an appropriate answer can be frustrating.”

Third, ethical issues with using ChatGPT were reported by student instructional designers. The primary concern is plagiarism. Students were unsure about the appropriate use and feared unintentional plagiarism. Victoria mentioned, “Before this class, I was hesitant about using ChatGPT due to the fear of plagiarism and not wanting to unknowingly put myself at risk.” Another ethical concern is copyright issues. Sally, an education specialist, shared her concerns, “I struggle with the idea that any use of ChatGPT relies on an outside source without appropriate credit. I can cite ChatGPT, but what about the individual sources it draws from?” Patrick added, “I wonder how AI tools like ChatGPT will impact areas typically done by artists, such as writing music, poetry, songs, and creating artwork. How will this affect copyright and ownership? Can AI own a copyright, or would the AI-generating tool own it?”

The fourth challenge is the possible negative consequences for cognitive learning and social interaction for student instructional designers. Some student instructional designers fear that over-reliance on ChatGPT could hinder cognitive learning. Hank stated, “Using ChatGPT takes away your ability to problem-solve on your own and think critically.” Another concern is that constantly seeking help from ChatGPT may reduce opportunities for peer interaction. Sally mentioned, “Using ChatGPT as a design and development partner could keep me from reaching out to peers for feedback or help. If I got validation from AI, why would I need to reach out to another person?” Lastly, despite ChatGPT’s support for instructional design, it cannot assist with hands-on instruction development. For instance, Scarlett mentioned, “ChatGPT could help generate ideas and brainstorm features for our mobile learning app, but it couldn’t create it.”

Discussions and Implications

This study explored student instructional designers’ experiences and perspectives on using ChatGPT for instructional design. The author analyzed reflection papers and learning artifacts from 17 graduate students using thematic analysis. The results revealed that ChatGPT has the potential to support various aspects of the instructional design process, including brainstorming, outlining designs, creating content and materials, designing learning activities, developing assessments, and enhancing text quality. These findings align with previous research by Choi et al. (2024) and DaCosta and Kinsell (2024), as well as Chng’s (2023) assertion that AI can transform instructional design practices. Chng (2023) proposed integrating AI into the ADDIE model (analysis, design, development, implementation, and evaluation), noting that AI could transform instructional design practices. In this current study, student instructional designers emphasized ChatGPT’s potential in the analysis and design stages, though they noted that ChatGPT could not assist in developing instructional products, requiring instructional designers to rely on their technological and hands-on skills. This unique finding reveals that concerns regarding ChatGPT replacing instructional designers could be alleviated, as instructional designers have the unique advantage of leveraging technologies to develop instructional materials and products. Moreover, due to the course structure of the four courses focusing on analysis, design, and development, the study did not extensively explore the implementation and evaluation stages, suggesting future research should examine student experiences across the full ADDIE process.

Student instructional designers in this study identified two primary benefits of using ChatGPT in instructional design: efficiency and effectiveness. Consistent with prior studies (Choi et al., 2024; Kumar et al., 2025), participants perceived ChatGPT as a valuable tool for reducing workload and expediting routine aspects of the design process. However, they also emphasized that such efficiency is contingent on users’ AI literacy, particularly their ability to craft effective prompts and critically evaluate outputs. In terms of effectiveness, participants reported that ChatGPT supported the improvement of their instructional design products by streamlining foundational tasks such as brainstorming, drafting, and refinement. This allowed them to dedicate greater attention to higher-order elements of design that require human judgment and creativity. These findings align with Onal and Kulavuz-Onal’s (2024) conclusion that ChatGPT is well-suited for generating certain instructional components, though it cannot replace human expertise. Furthermore, empirical studies (e.g., Karaman, 2024; Onal & Kulavuz-Onal, 2024) have demonstrated that integrating ChatGPT into instructional design can positively influence student learning outcomes, particularly when it is employed to scaffold lesson planning and assessment development. These findings suggest that ChatGPT can serve as a meaningful collaborator in instructional design when used judiciously. Instructional designers and educators should therefore consider ways to strategically leverage ChatGPT’s potential while simultaneously cultivating AI literacy skills to maximize benefits and mitigate risks (Choi et al., 2024; Parsons & Curry, 2024).

Despite these benefits, students reported several challenges in using ChatGPT for instructional design. One significant issue was the technical limitation of inaccuracy and hallucination, consistent with prior studies (Davis & Lee, 2023; Onal & Kulavuz-Onal, 2024). Due to these limitations, instructional designers should evaluate ChatGPT’s outputs (Davis & Lee, 2023) and exercise their judgment in design decisions (Boling et al., 2017). Ethical concerns, including potential plagiarism and copyright issues, were also reported. The literature frequently discusses these issues (Atlas, 2023; Gimpel et al., 2023; Luo et al., 2025; Office of Educational Technology, 2023; Rudolph et al., 2023). Therefore, it is crucial for instructors and instructional designers to create assessments that creatively evaluate students’ learning (Parsons & Curry, 2024). Another concern was that ChatGPT might hinder the learning process for student instructional designers, echoing worries from learners in diverse fields (Zhu, 2025b). More research is needed to examine when and how to use ChatGPT effectively to support student learning. Lastly, the study found that students did not fully understand ChatGPT’s potential, indicating a need for AI literacy education on its features, functions, potential uses, benefits, and ethical concerns to help student instructional designers leverage the technology effectively.

Limitations and Future Research

This study has several limitations to consider. First, it primarily focuses on student instructional designers from a single university in the U.S., so caution is necessary when generalizing the findings to diverse educational settings. Future research should expand to include other educational contexts. Second, participants’ years of professional experience as instructional designers were not collected. However, the participants were primarily novice instructional designers, and their experiences and perspectives may differ from those of more expert practitioners. Future research should compare novice and expert instructional designers to examine potential differences in how they use and perceive ChatGPT in instructional design. Third, this study utilized two data sources, reflection papers and learning artifacts, to enhance trustworthiness. Future studies could incorporate longitudinal reflections, interviews, or focus groups to gain a deeper understanding of student instructional designers’ perspectives. Fourth, the data were analyzed by a single researcher, which may raise concerns about internal validity. To enhance rigor and reduce potential bias, several strategies were implemented. First, a reflexive journal was maintained throughout the analytic process, from data familiarization to theme development and refinement. This journal systematically documented coding decisions, assumptions, and emerging insights, thereby providing a transparent record of the analytic process (Nowell et al., 2017). Second, the researcher revisited the data multiple times to clarify potential ambiguity and confirm consistency in interpretation. To mitigate coding fatigue, coding was conducted in multiple sessions with regular breaks, reducing the likelihood of error due to exhaustion. While the inclusion of a secondary coder would have further enhanced reliability, the combination of reflexive practice and iterative engagement with the data provided rigor and helped mitigate limitations associated with single-coder analysis.

Conclusions

This study explored student instructional designers’ experiences and perspectives on using ChatGPT for instructional design. The results revealed that ChatGPT has the potential to support various aspects of the instructional design process, the benefits, and the challenges. The study indicated that ChatGPT has the potential to support student instructional designers, which can significantly reduce the workload and increase efficiency and effectiveness. However, due to ChatGPT’s technical limitations, ethical use issues, and potential barriers to learning, human intervention, and AI literacy education are still necessary to ensure the reliability and quality of these outputs. These findings offer valuable insights into how GenAI can be effectively used for instructional design and how instructors can collaborate and leverage ChatGPT to provide better learning experiences for student instructional designers.

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