AI-Supported Career Sensemaking and Storytelling in Instructional Design Education

Introduction

Instructional design students often do work that looks and feels like the field long before they know how to name it. They design learning experiences, support colleagues, facilitate professional development, build instructional materials, analyze learner needs, and help people move through complex learning environments. Yet when they prepare resumes, cover letters, and portfolios, that work can become harder to see. Experiences that are rich in practice may appear thin on paper when students are unsure which terms employers recognize, how responsibilities differ across sectors, or how to connect prior roles to instructional design expectations.

This challenge is especially visible in instructional design and learning technology education, where students often enter from adjacent professional contexts and bring varied experiences into the field. Advising conversations frequently reveal a common pattern: students may struggle to see their own work as “real” instructional design, translate their experiences into employer-facing language, and navigate the wide variation in job titles and responsibilities across higher education, corporate training, nonprofit, healthcare, government, and other settings.

Generative artificial intelligence (AI) tools, including large language models (LLMs), are increasingly being used to support sensemaking, drafting, revision, and exploration across personal, educational, and professional contexts. These tools can generate language, surface patterns, offer alternative framings, and support iterative refinement of ideas and artifacts. At the same time, their outputs are probabilistic, context-dependent, and shaped by underlying training data. Users must exercise judgment as they interpret, adapt, verify, and apply AI-generated content.

This creates a practical tension for career preparation. AI can help students explore roles, identify patterns in job descriptions, experiment with language, and revise application materials. But career storytelling is not only a writing task. It is also an identity task, a judgment task, and a translation task. Students need to remain in control of their materials and their voice, and they often need human mentorship to determine whether the story they are telling is appropriate for the field, the role, and the employer.

This study explores one response to that tension: an AI-supported instructional guide called the AI Blueprint. Rather than beginning with a custom AI system, the project began with a design thinking process that centered students’ lived challenges and iteratively prototyped a structured workbook. The AI Blueprint invited students to work with generative AI tools within a scaffolded sequence of activities and to reflect on AI outputs using the Dimensions of AI Literacies (Gunder et al., 2024). In this project, AI was positioned as a supportive partner: a source of ideas, language, and alternative perspectives that required critical reflection, human judgment, and mentorship.

The study was guided by the following driving question:

How might we use generative AI to support career development and growth in instructional design?

Literature Review

This study is situated across four areas of scholarship and practice that shaped the design and examination of the AI Blueprint. First, design thinking provides a human-centered approach for addressing complex educational challenges through empathy, iteration, and prototyping. Second, career preparation and professional identity scholarship helps explain why instructional design students and early-career professionals may struggle to translate prior experience into employer-facing narratives. Third, emerging guidance on generative AI in career development clarifies both the promise and the limits of AI-supported résumé and cover letter work. Finally, the Dimensions of AI Literacies offers a plural, sociocultural lens for understanding reflective and responsible engagement with AI.

Framed by Design Thinking

Design thinking emerged from design disciplines as a human-centered approach for addressing complex problems through iterative exploration and solution development. It is commonly described as both an orientation and a process, emphasizing attention to people’s needs, experimentation, and learning through iteration rather than linear problem-solving (Stanford d.school, n.d.; Stevens, 2023). Over time, design thinking has been taken up across fields, including education, as a way to design supports and interventions in contexts where problems are ill-defined and solutions must evolve through use.

Contemporary descriptions of design thinking often reference five interconnected modes: empathize, define, ideate, prototype, and test. Together, these modes foreground empathy, sensemaking, and iteration (Stanford d.school, n.d.). These modes are not intended to function as a fixed sequence. Instead, design thinking acknowledges ambiguity as a productive condition and positions iteration as central to refining both the understanding of a problem and the possible responses to it (Stevens, 2023). Making ideas tangible through low-stakes prototypes and learning from feedback are central to this process.

Design thinking is particularly relevant when addressing challenges that involve uncertainty, variation across contexts, and the need to center users’ lived experiences. Career preparation in instructional design reflects these characteristics. Roles vary widely across sectors, expectations shift between employers, and students must interpret and translate prior experiences into professional narratives. From a design thinking perspective, these challenges call for approaches that support exploration, reframing, and iterative refinement rather than prescriptive solutions.

In this study, design thinking functions as a lens for understanding the development of the AI Blueprint. By beginning with students’ experiences, articulating a focused design challenge, and iteratively testing a structured instructional guide, the project aligns with a design thinking orientation that prioritizes usefulness, adaptability, and responsiveness to users. This framing establishes a foundation for examining career preparation as a design problem and situates the AI Blueprint within a broader tradition of human-centered educational design.

Career Preparation and Professional Identity in Instructional Design

Career preparation in instructional design is shaped by the field’s broad range of roles and sector-specific expectations. Instructional designers work across higher education, corporate training, nonprofits, healthcare, government, and other settings, often under varied titles and responsibilities. This variation requires applicants to translate diverse experiences into language that aligns with employer expectations (Peck, 2025). For students and early-career professionals entering the field from adjacent roles, this translation work is closely tied to professional identity formation.

Practitioner and institutional guidance consistently emphasizes alignment between applicants’ experiences and the terminology, frameworks, and competencies valued in instructional design. Employers look for evidence of transferable skills such as communication, problem-solving, analysis, planning, and design. Applicants are often expected to articulate those skills through the language of the field and support them with concrete outcomes (Peck, 2025; Slade, 2020). Resumes, cover letters, and portfolios are positioned as complementary artifacts that work together as a professional communication system, helping applicants communicate relevance, judgment, and readiness for practice (Massachusetts Institute of Technology Career Advising & Professional Development, 2025; University of Southern California Career Center, 2025).

Resumes, cover letters, and portfolios also need to work together. Effective application materials do more than list experience in separate places. They tell a cohesive professional story that situates prior experiences within a clear career trajectory (Gain, 2023; Gover, 2020). Each application piece has its purpose within that trajectory. For example, cover letters provide space for contextualizing experience and demonstrating communication and judgment, while portfolios offer tangible evidence of design process, problem framing, and learning through iteration (Bloom, 2023; Braven, 2019; Southern Methodist University, Simmons School of Education & Human Development, 2024).

For instructional design students and early-career professionals, this work is often difficult. Many struggle to recognize how prior experiences “count” as instructional design and to articulate their value with confidence. While existing guidance offers practical advice, less attention has been given to structured, research-informed approaches that support sensemaking and storytelling as developmental processes. This gap becomes increasingly important as emerging tools, including generative AI, shape how individuals explore roles and prepare application materials.

AI Use in Career Preparation and Development

Guidance from career centers, practitioners, and media sources increasingly acknowledges generative AI as a tool that can support aspects of career preparation, particularly drafting, revising, and tailoring job application materials. Institutional guidance often frames AI as useful for generating initial drafts of resumes and cover letters, suggesting action verbs, and experimenting with alternative phrasing, while cautioning against submitting AI-generated materials without careful review and revision (University of Delaware Career Center, 2025). These sources emphasize that AI outputs must be evaluated for accuracy, relevance, and alignment with the position and organizational context.

Across practitioner and media-oriented accounts, a consistent theme emerges: AI is most useful as a starting point rather than a finished product. Phillips (2025) cautions that keyword optimization and formatting can help materials pass initial screening processes, but overly generic or robotic language can undermine credibility and authenticity. Similarly, career guidance stresses the importance of enriching AI-generated drafts with specific details, including context, constraints, actions, and outcomes. Without this human refinement, AI-generated materials risk appearing interchangeable or misaligned with the applicant’s actual experience.

Instructional design-specific guidance offers more granular insight into how AI may support career preparation within the field. Peck (2023) demonstrates how generative AI can assist instructional designers in reframing resumes, practicing interview responses, and translating experience from adjacent roles into instructional design language. At the same time, this work highlights significant limitations, including inconsistent outputs, loss of quantification, awkward phrasing, and the need for iterative prompt refinement. Peck emphasizes that effective use of AI requires both front-end human input, such as precise prompts and role framing, and back-end human judgment, including editing for accuracy, voice, and quality.

Taken together, this applied literature positions generative AI as a potentially valuable support for career preparation, but one that depends heavily on structure, iteration, and human oversight. These sources converge on a shared caution: AI can assist with exploration, drafting, and revision, but it cannot replace human judgment, contextual knowledge, or mentoring, particularly for students and early-career professionals who are still developing confidence and interpretive skill. This framing underscores the need for structured, reflective approaches to AI use in career preparation, which the present study examines.

The Eight Dimensions of AI Literacies

This project is situated within the Dimensions of AI Literacies developed by Gunder, Herron, Weber, Chelf, and Birdwell (2024). Rather than positioning “AI literacy” as a singular or universal competency, this work advances a pluralistic view of AI literacies as interconnected, socioculturally situated practices that combine skillsets and mindsets as individuals engage with AI in personal, professional, and educational contexts. From this perspective, engaging with AI involves not only using tools effectively but also interpreting outputs, exercising judgment, and navigating ethical, cultural, and social considerations across contexts.

The Dimensions of AI Literacies taxonomy articulates eight interrelated areas of engagement: cultural, cognitive, constructive, communicative, confident, creative, critical, and civic. Together, these dimensions offer a shared vocabulary for sensemaking and reflection around AI use (Gunder et al., 2024). These dimensions are not intended to operate in isolation. Their value lies in how they connect and reinforce one another as individuals move between contexts and purposes.

In this study, the AI Blueprint draws on these dimensions as reflective prompts embedded throughout the instructional guide. Students were invited not only to work with AI to explore roles and refine application materials, but also to consider how their interactions with AI reflected issues of voice, accuracy, confidence, and responsibility as they developed and communicated their professional stories.

Together, these bodies of literature establish the conceptual grounding for the design of the AI Blueprint and inform the methodological approach used to examine its use in practice.

Methods

This study employed an exploratory qualitative design centered on the development and use of an instructional guide, the AI Blueprint, designed to support career sensemaking and storytelling for instructional design students and early-career professionals. The project examined both the design of the Blueprint and participants’ experiences engaging with it, with particular attention to how AI use unfolded when embedded within guided reflection and human judgment. The sections that follow describe the AI Blueprint, the study context and participants, the data collected through its implementation, and the analytic approach used to interpret participants’ reflections.

AI Blueprint Instructional Guide

The AI Blueprint Instructional Guide was designed as a structured workbook that integrates generative AI, reflective practice, and career preparation for instructional design and learning technology roles.

The Blueprint organized participants’ work into five steps: (1) initial reflections on sector knowledge, current materials, and comfort with AI; (2) exploration of the instructional design field and a sector of interest; (3) identification of a current job posting aligned with that sector; (4) alignment and revision of resumes, cover letters, and portfolios to that posting; and (5) final reflections on both the process and participants’ evolving perspectives on AI, career readiness, and application materials.

Across Steps 2–4, the Blueprint provided editable starter prompts and asked participants to use a single AI chat thread in a tool of their choice so the tool could build contextual awareness over time. After each major interaction with AI, including exploring sectors, locating roles, and revising materials, participants completed guided critical reflection activities adapted from the Dimensions of AI Literacies (Gunder et al., 2024). These activities asked participants to consider cultural, cognitive, constructive, communicative, confident, creative, critical, and civic aspects of their AI use. In this way, the Blueprint functioned both as a career development scaffold, helping participants explore sectors, identify positions, and refine materials, and as a structured mechanism for examining how AI supported, complicated, or reshaped their career sensemaking and storytelling.

Participants and Context

The study took place in a fully online, asynchronous master’s program focused on instructional design and learning technology at a regional comprehensive university in the Midwest. The program primarily serves adult learners who are employed full-time, looking to advance their careers in instructional design and learning technology roles or seeking to enter the field.

Two graduate students (n = 2) volunteered to participate after a program-wide invitation was posted in the learning management system. Both students were actively preparing for, or considering, transitions into instructional design roles and expressed a desire to better articulate their skills, translate prior experiences into the language of the field, and strengthen their resumes and cover letters.

The study received Institutional Review Board approval from the host institution. Participation was voluntary and not connected to any course grade or requirement.

Data Collection and Analysis

Data for this study consisted of participants’ written responses and reflections embedded directly within the AI Blueprint Instructional Guide. As participants worked through the five steps of the Blueprint, they documented their interactions with generative AI, their evolving understanding of instructional design roles, and their reflections on résumé and cover letter alignment, confidence, and AI use. These reflections served both as learning artifacts for participants and as the primary data source for the study.

Following completion of the Blueprint activities, participants’ written responses were exported into a collaborative coding document and assigned a pseudonym. Data were analyzed using an exploratory qualitative coding process informed by grounded approaches. Initial open coding focused on identifying recurring actions, perceptions, and evaluative statements related to career sensemaking, narrative development, AI affordances, and AI limitations. These codes were then grouped into broader themes that captured shared patterns across participants’ experiences.

Throughout the analysis, attention was paid to how participants described the role of generative AI in their work, where AI supported or constrained sensemaking and storytelling, and how reflection and human judgment shaped their use of AI outputs. Analytic decisions were documented to maintain transparency, and interpretations were reviewed collaboratively to support trustworthiness.

Findings

Working with the AI Blueprint surfaced clear patterns in how participants made sense of instructional design roles, articulated their value, and navigated AI-supported career preparation. These findings are grounded in participants’ written reflections and highlight both the affordances and limitations of generative AI when paired with structured guidance and human judgment. Analysis of participants’ reflections revealed five consistent patterns in how the AI Blueprint supported career sensemaking, storytelling, and AI use.

Gaining Clarity About Roles and Language

Participants reported that using AI within the structured Blueprint helped them better understand the range of instructional design roles and the language used to describe them, particularly in sectors where titles and responsibilities vary widely.

One participant reflected on how this increased their understanding, but that they likely won’t feel fully confident until they’ve spent time in the field:

“I’m probably somewhere between ‘Confident’ and ‘Very Confident’ [regarding understanding roles in their intended sector] now that I’ve had a chance to see what the different roles/titles are for my field. I also have a better sense of their responsibilities and differences from each other. I don’t think I’ll be ‘Very Confident’ until I’m in the field.”

Another emphasized how seeing role variation reduced uncertainty about where to focus their search:

“I liked seeing the main roles found in IDLT of higher education and getting a snapshot of what a typical day was like. I also appreciate having a list of job roles I can search for besides ‘instructional designer.’”

Rather than narrowing options prematurely, the Blueprint supported exploration and helped participants search more intentionally for positions using field-specific language.

Recognizing and Articulating Value

Across reflections, participants described underestimating their experiences prior to the Blueprint, even when those experiences aligned closely with employer expectations. The value mapping and revision activities helped them surface the need for concrete examples and documentation of the impact of their work.

One participant noted:

“I think I need to work on adding some concrete examples to my resume and I will track the KPIs.”

Another reflected on how the activity helped them to see the point in their story:

“I think the cohesion and conciseness are working well in my materials. I also think I’m doing better at showing my experiences by using stories that have a point.”

These reflections suggest that the value of the Blueprint extended beyond polishing language. The Blueprint supported participants in recognizing their work as legitimate instructional design experience and sharing it as part of a cohesive professional story. It also helped them see the importance of concrete experiences in telling that story and tracking outcomes as part of it.

Support for Storytelling Across Materials

Cover letter writing emerged as a particularly challenging genre, especially when roles differed significantly across postings. Participants described AI-supported expansion as helpful when paired with reflection and revision.

One participant shared nervousness about the cover letter component:

“The cover letter is probably my weakest point… with the roles being so different between job postings, I need to find confidence in writing a great cover letter each time.”

Later, reflecting on AI support, the same participant noted learning about how to use the cover letter to extend the story of the resume bullet points:

“I liked the advice AI gave me on how to take my resume bullet points and write a story [in a cover letter] connected to what they are looking for.”

At the same time, participants were attentive to voice and authenticity, noting when AI-generated language no longer reflected how they would describe their own experiences:

“It did create some variety in words that I normally wouldn’t think of myself (e.g., orchestrated) but I wonder if it doesn’t sound like me.”

Taken together, these reflections position AI as useful for idea generation and language variation, with human judgment remaining central to preserving voice and coherence across materials.

Where AI Fell Short and Why That Mattered

Participants were consistent and explicit about the limitations of AI tools for identifying job postings. Links were often outdated, misaligned, or inaccurate, which reduced trust in this use of AI.

As one participant explained:

“It struggled to provide me with jobs that were relevant and actually available.”

Another added:

“Not a single job it mentioned was listed on the University’s job site.”

These limitations did not undermine the overall value of the Blueprint. Instead, they prompted participants to adopt a more critical stance toward the strengths and weaknesses of AI outputs. This reinforced the importance of verification and of understanding how tools operate, what data they draw from, and where they are ill-suited.

Confidence, Preparedness, and the Human in the Loop

By the end of the process, participants reported increased confidence in understanding roles and preparing application materials. At the same time, they emphasized that AI was most effective as a supportive partner, not a standalone solution or a replacement for an experienced mentor.

One participant reflected on how much they would use AI in their process:

“I think AI works as a good feedback tool or a tool to generate ideas for alignment. I would not fully rely on AI to create my documents.”

Another noted the importance of mentorship for sensemaking:

“It’s hard to fact-check AI without collaborating with someone with experience in the field.”

Across reflections, a clear pattern emerged: AI-supported career preparation was most effective when paired with structure, critical reflection, and human guidance. The Blueprint helped participants position AI as one contributor in a broader ecosystem of tools, relationships, and literacies that shape career readiness in instructional design.

Discussion

Findings suggest that when used as part of a reflective, human-centered process, generative AI can support instructional design students and emerging professionals as they identify roles and sectors of interest, articulate their value, and develop coherent professional stories. Participants’ experiences with the AI Blueprint align with scholarship emphasizing the challenges novice instructional designers face in translating prior experience into field-specific language, demonstrating impact, and navigating diverse role titles and sectors (Peck, 2025; Slade, 2020).

The Blueprint’s mapping and revision activities supported participants in identifying valuable transferable experiences, aligning them with employer expectations, and beginning to tell more cohesive professional stories across resumes and cover letters. This echoes calls in the literature for concrete, narrative-oriented supports for career preparation in applied fields (Braven, 2019; Gover, 2020).

At the same time, the study complicates more optimistic accounts of AI as a turnkey solution for career preparation. Participants were critical of AI-generated job postings and wary of generic or inauthentic language in their materials. These concerns reinforce cautions about overreliance on AI and the risks of treating AI outputs as authoritative (Phillips, 2025; University of Delaware Career Center, 2025).

When paired with reflective prompts and human mentoring, however, participants described AI as a helpful partner for ideation, drafting, and revision rather than as a shortcut or replacement (Peck, 2023). Their reflections map closely onto the Dimensions of AI Literacies (Gunder et al., 2024), particularly constructive, communicative, confident, and critical literacies. Across the study, participants were not simply using AI to produce application materials. They were learning how to question outputs, protect voice, interpret employer expectations, and make choices about what belonged in their professional stories.

This pattern underscores the importance of design approaches that foreground human judgment, context, and care. For instructional design education, the implication is not simply that students should or should not use AI in career preparation. Rather, students need structured opportunities to use AI reflectively, test its outputs against field expectations, and stay responsible for the stories they tell about their own work.

Design Implications

Taken together, these findings point less to a single solution and more to a set of design considerations for supporting students as they prepare for instructional design careers in an AI-enabled world. Rather than centering tools alone, these implications focus on the conditions that helped participants make sense of roles, recognize their value, and use AI responsibly: structure, reflection, human mentorship, and attention to voice.

For Faculty Teaching in Instructional Design and Educational Technology Programs

Faculty are often closest to the project work that students later need to translate for employers. Course assignments, design projects, facilitation experiences, and peer feedback activities can all become evidence of professional readiness when students are supported in recognizing and articulating their value.

Reflection

• Where in your courses might students translate their work for audiences beyond the classroom?
• Where might they need more structured support to do that well?

Action

• Build intentional moments where students practice translating projects, facilitation, or design work into résumé bullet points, cover letter examples, portfolio language, or short professional narratives.
• When introducing AI, frame it as a revision and sensemaking partner, and pair its use with reflection on accuracy, voice, and alignment.

For Program Coordinators and Administrators

Program-level supports can help students see career preparation as part of their learning experience rather than as an activity that happens only after coursework is complete. This is especially important in a field where roles, titles, and sector expectations vary widely.

Reflection

• How explicitly does your program help students understand the range of roles they might pursue?
• How explicitly does your program help students understand the language those roles use across sectors?

Action

• Integrate structured career exploration and application support into program experiences, rather than treating it as an add-on or outsourcing it entirely.
• Support faculty in modeling responsible AI use by providing shared approaches, such as reflection prompts, AI literacies-based questions, and adaptable activity prompts that can fit different courses and contexts.

For Students and Early-Career Professionals

Students and early-career professionals often bring more relevant experience than they initially recognize. AI can help surface patterns, suggest language, and offer alternative ways to frame that experience, but the final story still needs to be theirs.

Reflection

• Where might you be underestimating the value of work you already do?
• Where might you be assuming that experience does not count because it comes from a different sector or does not match a job title exactly?

Action

• Use AI to help surface patterns, identify keywords, and translate your experience into the language of employers. Then revise with your own judgment and voice in mind.
• Pair AI feedback with human feedback from mentors, peers, instructors, or professionals in the field, especially when you are unsure what to trust.

Conclusion

This study examined how an AI-supported instructional guide supported instructional design students and early-career professionals in career sensemaking and storytelling. Working through the AI Blueprint, participants used generative AI to explore roles, align experiences with employer expectations, and refine application materials while reflecting on accuracy, voice, and judgment. The findings suggest that AI can meaningfully support career preparation when it is embedded within structured guidance, reflective practice, and human mentorship.

Across participants’ reflections, a consistent takeaway emerged: AI was most useful when it functioned as a supportive partner rather than a standalone solution. Structure, opportunities for reflection, and access to human mentorship shaped how participants evaluated AI outputs, maintained authenticity, and developed confidence in articulating their professional value.

This study has several limitations. It involved a small, self-selected sample within a single instructional design program and relied on written reflections as the primary data source. Findings are therefore not intended to be generalizable, but to surface early insights into how AI may support career sensemaking and storytelling when used within a guided, human-centered process.

Future research could extend this work by implementing the AI Blueprint with larger and more diverse groups, incorporating additional qualitative methods, or examining longer-term use over time. The AI Blueprint itself was never intended to be a final product, but a starting point. Based on what was learned here, future iterations may include a short course or a custom GPT that preserves the project’s reflective and human-in-the-loop orientation to career preparation.

This work points toward a future in which AI does not replace the human work of career preparation, but helps make that work more visible, intentional, and reflective. For students and early-career professionals, the goal is not to let AI tell their story for them. It is to use AI and mentorship to recognize the value of their experience, translate it for the field, and tell that story in a voice that remains their own.