GIFT-AI: designing generative AI tools for organizational communication

This article presents a Great Ideas for Teaching (GIFT) activity in which students design and evaluate a custom generative AI (GenAI) tool grounded in organizational communication theory. Working in teams, students identify a workplace communication challenge, apply a relevant theoretical framework, and develop a GenAI prototype to address or improve that process. The assignment is structured around experiential learning principles, encouraging students to learn through iteration, feedback, and reflection. It culminates in an in-class pitch and a documentation guide that outlines the tool’s design, intended use, and ethical implications. By combining theoretical analysis with prompt engineering and design thinking, the activity helps students critically engage with the role of AI in organizational life. It also equips them with practical experience to assess, implement, and communicate the value of AI-mediated communication tools.

1 Intended course

This assignment fits undergraduate or graduate courses within organizational communication, management, leadership, or communication and technology. It is designed for face-to-face instruction but can be adapted for hybrid or online settings by using synchronous video meetings for presentations and asynchronous collaboration tools (e.g., Google Docs, Slack, or Canvas) for collaboration. It works best with a class size of 15–30 students, who can be arranged into teams of four to five members.

By the end of the activity, students will have learned to leverage an organizational communication theory to address a real or hypothetical workplace process, develop practical skills in designing and refining a custom generative AI tool, consider the ethical and responsible dimensions associated with generative AI design, and produce a documentation guide for their AI prototype. Generative AI (GenAI) tools are a subset of AI technologies that can generate content (in this case, using text) in response to user inputs, making them versatile tools for supporting or partially automating workplace communication tasks.¹ This sequence ensures that students not only experiment and gain literacy with widely used AI tools but also consider how their designs might integrate into broader organizational work processes.

The activity is best suited for courses where students have been introduced to basic communication or organizational theory, and it works especially well when scheduled later in the term after they have had time to engage with foundational concepts. Instructors may choose to offer a brief theory primer or recap at their discretion to support student engagement.

The activity takes place across two 75-min sessions, with an additional 2–3.5 h of out-of-class teamwork expected between meetings.

2 Objectives and proposed learning outcomes

This activity is designed to bridge theory and practical application by having students create and critically evaluate a generative AI tool that addresses a specific workplace communication challenge. Through this project, students draw on organizational communication theory, develop technical fluency with GenAI platforms, and reflect on the ethical and collaborative implications of AI use in practical organizational contexts. The assignment reinforces key ideas from the course while developing relevant technical and collaborative skills.

By the end of this activity, students will be able to:

• Apply an organizational communication theory to analyze and address a real or hypothetical workplace process

• Design and test a generative AI tool to automate a communication-related task(s)

• Evaluate the practical and ethical implications of GenAI in workplace settings, including issues of accountability and transparency

• Collaborate in a team to iteratively develop and present a functional, theory-informed GenAI prototype and documentation

3 Theoretical and pedagogical orientation

In today’s communication landscape, students are increasingly expected to understand and collaborate with AI technologies (World Economic Forum, 2025). Yet, few classroom assignments offer structured, hands-on opportunities to build these skills. According to a recent survey by Cengage, 70% of graduates want generative AI training integrated into their courses, and one in two report feeling unprepared to use AI tools in the workplace (Inside Higher Ed, 2024).

Building on this need, recent work on AI-mediated communication provides a foundation for designing assignments that engage students in applied, organizational use of generative AI. Hancock et al. (2020) define AI-mediated communication as “interpersonal communication in which an intelligent agent operates on behalf of a communicator by modifying, augmenting, or generating messages to accomplish communication goals” (p. 89). Across work contexts, scholars have explored how AI reshapes everyday communication workflows (Faraj et al., 2018; Laapotti and Raappana, 2022; Cruz, 2024). For example, GenAI tools can automate tasks such as generating onboarding emails, summarizing internal updates, or drafting routine reminders—functions that were previously handled by employees.

This assignment also draws on experiential learning by engaging students in the design, testing, and evaluation of GenAI tools for workplace scenarios. As Beard and Wilson (2018) note, experiential learning emphasizes reflection, iteration, and application. These principles are embedded in the assignment structure: students iteratively experiment with prompts, refine their tool based on feedback, and produce a documentation guide that captures both technical and theoretical reasoning (see Appendix A, Section 3). In doing so, students move between theory and practice—testing assumptions, revising ideas, and critically assessing how their tool operates within organizational contexts (see Appendix B). For example, in the “Configuration Details” and “Assessment” sections, students are asked to explain how their design connects to a workplace need and reflect on how well the tool works in use.

This GIFT engages students in thinking about how GenAI transforms the communication practices that are central to organizational functioning. Drawing on theories such as organizational socialization, leader–member exchange (LMX), and conflict management, students examine how AI-generated messages might support or complicate relational dynamics, message credibility, or knowledge coordination. For instance, a group might use a theory of socialization to guide the design of a GenAI tool that produces onboarding content tailored to new employee experiences. Rather than treating theory as an abstract concept, the activity positions it as a practical lens for assessing and designing communication processes in AI-enhanced environments.

4 Implementation

The assignment is implemented in two sessions: one for introducing the task and brainstorming, and another for presenting their custom GenAI tool that was collaboratively developed in the interim.

4.1 Session 1: theoretical grounding and project start (75 min or 1 class meeting)

The initial session introduces the class to the assignment, its objectives, and expected deliverables. Students are divided into teams of four to five members and receive the assignment guide (see Appendix A). This guide provides a detailed overview of the task, including step-by-step instructions for accessing the Google Gemini platform, creating a custom generative AI prototype, and submitting final materials. It also includes example prompts, a checklist of documentation elements, and links to technical support resources (see Appendices A, B).

Instructors are encouraged to familiarize themselves with Gemini’s Gem builder and the included resources prior to the session, but it is also worth noting that many students are already regular users of GenAI platforms. In practice, students often bring creative prompting strategies and tool knowledge into the process, and teams can help one another troubleshoot or refine their tools collaboratively as part of the assignment’s learning experience.

Next, the instructor should revisit a few key organizational communication theories discussed in previous class sessions and explain how these theories help us understand workplace communication processes. These can be pulled from previous lectures, course readings, or co-constructed with students through in-class discussion. For instructors who wish to explore a broader range of theories, the Handbook of Organizational Communication Theory and Research (Miller and Poole, 2024) offers a useful overview of organizational communication theories and frameworks.

The following sample theory illustrates how a single framework can help students clarify the communication process they are trying to automate, as well as identify key tasks, skills, and stakeholders involved. While not required, this structure offers one way to bridge theory and practice and can help teams focus their GenAI tool design.

Sample theory: anticipatory socialization

Anticipatory socialization refers to the process by which individuals acquire the knowledge, skills, and norms needed to effectively integrate into an organization—even before their first day on the job. This phase shapes expectations, builds clarity, and helps establish a smooth entry. When designing a GenAI tool, this theory can guide students to think critically about what information new hires need, who provides it, and how it gets communicated.

• Potential processes: onboarding and training efforts that prepare new employees before day one. This includes explaining policies, outlining responsibilities, and introducing team dynamics

• Relevant tasks: building structured schedules, providing training documents, crafting welcome emails, and highlighting performance expectations

• Relevant skills: communication clarity (e.g., making policies accessible), mentorship (e.g., helping new hires navigate early challenges), and adaptability (e.g., tailoring messaging to individual roles)

• Potential stakeholders: HR staff, team leads, and managers who guide and support the onboarding process

The instructor introduces generative AI as a tool with the potential to optimize routine workplace tasks while emphasizing its limitations. For example, while generative AI can streamline processes like drafting schedules or standardizing feedback, it also carries risks, including the generation of inaccurate or biased content. Instructors encourage students to remain critical of AI outputs, highlighting the importance of transparency, accountability, and adherence to ethical protocols (e.g., Cruz, 2024; Sundar and Liao, 2023).

Initial brainstorm: following the conceptual grounding, students work in teams to identify a specific organizational communication challenge that could benefit from AI-driven support. This may include routine communication tasks (e.g., onboarding emails), interpersonal dynamics (e.g., conflict management or feedback delivery), or strategic coordination (e.g., cross-functional collaboration). Teams are encouraged to select challenges that are familiar, meaningful, and feasible to address within the scope of the assignment.

Each team then identifies a relevant organizational communication theory to help frame the selected challenge. The purpose of this step is to guide students in using theory to analyze the communication process and inform the development of their GenAI tool. For example, a team working on onboarding might apply socialization theory to consider how new employees learn organizational norms. A team addressing cross-functional collaboration may draw on transactive memory systems to account for how groups share and coordinate knowledge. Again, instructors may revisit prior lecture content or facilitate an in-class discussion to help students recall and apply theories previously covered in the course.

Once a theory and challenge are selected, teams begin outlining their tool’s function and scope. They define input parameters, intended outputs, and any contextual considerations needed to ensure their GenAI tool is aligned with the organizational communication goal. For example, a team designing a welcome message generator may specify tone, user inputs (e.g., department, start date), and accessibility considerations.

Teams then draft initial prompts to test how the GenAI tool responds. These prompts serve as the building blocks of the tool’s logic and functionality. For example, in this hypothetical situation:

Write a welcome email from an AI assistant for new summer interns joining the marketing department at XYZ Agency. The email should include basic information such as the office location in Downtown Chicago, a 9:00 AM start time, and instructions to meet at the front desk. Mention who they should check in with, what to expect on their first day, and offer a few friendly tips to help them feel prepared. Use a warm and welcoming tone.

Teams experiment with their AI tools during this session, testing how well the responses match the task and reflecting on whether the tool supports or complicates communication in the scenario. The session concludes with a short class discussion where teams share what problem they are tackling, which theory being used, and one example of a GenAI prompt they tested.

The session concludes with the instructor outlining the next steps. Teams are tasked with refining their prompts, beginning practicing their documentation, and preparing for further experimentation to develop a full prototype before the next class.

4.2 Between sessions: prompt refinement and documentation development (approximately 2.5–3 h)

After the first session, teams spend time outside of class refining their GenAI prototypes. They use Google Gemini Gems or an equivalent Generative AI platform to test prompts, adjusting both content and parameters as needed. Students record their AI’s outputs and note any limitations or inaccuracies, iterating prompts to achieve improved results. This process underscores how human oversight remains fundamental to AI design. Students are encouraged to stay alert to the possibility that AI outputs can produce unintended consequences or reflect unexpected biases.

At this stage, students begin constructing a documentation guide that explains the scope and purpose of their tool. They are encouraged to include an overview of the organizational challenge, references to the theoretical framework, and a summary of the technical configurations and specific prompts used. The guide should also address ethical concerns, such as how to handle potential biases, maintain user privacy, and delineate which tasks might still require human intervention. Students may add a “maintenance and future enhancements” section, articulating how their tool might evolve over time, receive updated data inputs, or require disclaimers to avoid user misinterpretation. This comprehensive approach ensures that the tool they develop is not only feasible but also transparent and adaptable.

4.3 Session 2: presentations, feedback, and reflections (75 min or 1 class meeting)

In the second session, students reconvene with their refined GenAI prototypes. Each team presents their tool as a pitch to the class, beginning with a clear restatement of the workplace challenge they sought to address. Teams then explain how an organizational communication theory informed their approach, articulating how the theory helped them define the problem and guide their tool’s design.

Presentations walk the class through the tool’s core functionality: what it is designed to do, how it works, and what type of output it produces. Teams highlight key strengths as well as persistent limitations (e.g., vague responses, off-target phrasing, or difficulties generating nuanced language). Where appropriate, teams also note any disclaimers or instructions they would include to prevent misuse or confusion.

Following each presentation, the instructor invites questions from the class. These exchanges often focus on practical matters, such as how the tool would integrate into existing workflows or who would be responsible for maintaining and updating prompts. Ethical or relational queries often arise, prompting students to articulate how they would ensure that users understand AI’s boundaries, remain vigilant against bias, or navigate delicate communication tasks requiring emotional nuance. These exchanges encourage students to think beyond the prototype and reflect on how the tool would operate in live contexts, especially when communication tasks involve emotion, ambiguity, or power dynamics.

After all teams have presented, the instructor leads a broader reflection. This discussion returns to the communication theories at play and invites students to assess how successfully their tools embodied these concepts. Instructors can also prompt students to think about when and why human communication remains irreplaceable, especially in contexts requiring empathy, creativity, or critical judgment. Throughout this dialogue, students are reminded that AI tools are not neutral or purely technical. Rather, they are embedded in—and can reshape—communication practices, social expectations, and power structures (e.g., Cruz, 2024; Faraj et al., 2018; Laapotti and Raappana, 2022). By reflecting critically on their own design choices and outcomes, students gain a deeper appreciation of how GenAI might assist or complicate organizational life.

4.4 Appraisal and limitations

Conducting this assignment over two course sessions offers several advantages. By giving students time between classes to refine prompts and develop their documentation, instructors encourage more thoughtful and intentional design. Students are better positioned to explain the rationale and efficacy behind their tool’s outputs and produce a polished documentation guide with clear, intentional links to theory. In practice, students have created custom GenAI tools for a range of workplace tasks: brainstorming aids for teaming, advice bots for managing conflict with supervisors, email templates for global, intercultural communication, and employee onboarding assistants. In practice, many student teams move fluidly between completely automating or augmenting parts of a task as they refine their prompts and evaluate outputs. This flexibility is a strength of the assignment, allowing students to engage with GenAI tools at varying levels of complexity while remaining grounded in theory and context. Instructors can decide how explicitly to distinguish between these approaches based on course goals and student readiness.

Course feedback from students highlights how the activity supports this experimentation and experiential learning. One student noted, “We got a chance to test out different versions and figure out what to automate and what not to. It made me think about the differences between what we should keep human and what to give to the AI.” Another wrote, “While my personal stance on AI has not changed, I feel more knowledgeable and prepared to enter a job and think about automating [something, a task] intentionally.” A third student emphasized the applied value of the project: “I was impressed to learn about the different ways organizations use AI into their workflows and take a chance to make my own and customize it.” These reflections underscore the assignment’s impact—not only in building AI literacy, but in allowing them to apply theory, navigate design choices, and reflect on the broader organizational ecosystem of emerging technology at work.

This activity is also adaptable to a range of organizational contexts beyond the classroom. Instructors may consider tailoring the project to scenarios relevant to press agencies, nonprofits, or global corporations, depending on course goals. It is worth noting that GenAI tools vary in their performance across languages and cultural contexts, and students may observe different results depending on how their prompts or tools are situated. These variations can provide additional opportunities for reflection on communication norms, access, and scale.

That said, instructors should be aware of a few limitations. Some students may initially struggle with prompt engineering, particularly if they are unfamiliar with GenAI tools. Practicing prompting collectively and troubleshooting together during the first session can help ease this learning curve. Additionally, time constraints may become an issue if teams pursue overly ambitious or complex designs. In these cases, instructors may need to encourage students to focus on a specific communication. Finally, instructors may wish to remind students that a tool’s performance can vary significantly as AI tools evolve over time, making it essential to regularly revisit and update any tool that is introduced into an organization.

5 Conclusion

Designing a GenAI tool for organizational communication is a multifaceted learning experience that invites students to connect theoretical insight with hands-on practice. Students begin to see that AI integration is not merely about efficiency gains, but also about ensuring alignment with organizational goals and the complexities of communication interactions. Learners discover how generative AI can alter communication flows—sometimes productively, sometimes problematically—and they emerge more prepared to critically assess AI’s presence in contemporary workplaces. Through this iterative and theory-driven approach, the assignment serves as a practical tool for shaping students into thoughtful, adaptable, and responsible communication professionals.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

IC: Writing – original draft, Writing – review & editing, Conceptualization, Methodology.

Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author declares that no Gen AI was used in the creation of this manuscript.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcomm.2025.1554052/full#supplementary-material

Footnotes

1. ^This project primarily centers on automation, where a GenAI tool independently carries out a task—for instance, generating a complete onboarding email based on a prompt. In contrast, augmentation refers to scenarios where AI plays a supporting role, like offering edits or suggestions to a message the user has already started. Instructors may choose to frame student projects around either mode depending on their course goals. While the assignment focuses on automation, many teams naturally engage in augmentation when refining prompts and adjusting outputs. It is also common for users to blur the line between these two uses depending on the level of oversight and iteration involved. For additional clarity on these distinctions, instructors can refer to Hancock et al. (2020) on AI-mediated communication and Mollick (2024) for applications of GenAI tools.

References

Beard, C., and Wilson, J. P. (2018). Experiential learning: A handbook for education, training and coaching. 3rd Edn. New York, NY: Kogan Page.

Google Scholar

Cruz, I. F. (2024). Expert-AI pairings: expert interventions in AI-powered decisions. Inf. Organ. 34:100527. doi: 10.1016/j.infoandorg.2024.100527

Crossref Full Text | Google Scholar

Faraj, S., Pachidi, S., and Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Inf. Organ. 28, 62–70. doi: 10.1016/j.infoandorg.2018.02.005

Crossref Full Text | Google Scholar

Hancock, J. T., Naaman, M., and Levy, K. (2020). Ai-mediated communication: definition, research agenda, and ethical considerations. J. Comput.-Mediat. Commun. 25, 89–100. doi: 10.1093/jcmc/zmz022

Crossref Full Text | Google Scholar

Inside Higher Ed (2024). New report finds recent grads want AI to be part of their college education. Available online at: https://www.insidehighered.com/news/tech-innovation/artificial-intelligence/2024/07/23/new-report-finds-recent-grads-want-ai-be (accessed July 23, 2024).

Google Scholar

Laapotti, T., and Raappana, M. (2022). Algorithms and organizing. Hum. Commun. Res. 48, 491–515. doi: 10.1093/hcr/hqac013

Crossref Full Text | Google Scholar

Miller, V. D., and Poole, M. S. (Eds.) (2024). Handbook of organizational communication theory and research. Berlin: De Gruyter Brill.

Google Scholar

Mollick, E. (2024). Co-intelligence: Living and working with AI. London: Penguin Books.

Google Scholar

Sundar, S. S., and Liao, M. (2023). Calling BS on ChatGPT: reflections on AI as a communication source. J. Commun. Monogr. 25, 165–180. doi: 10.1177/15226379231167135

Crossref Full Text | Google Scholar

World Economic Forum (2025). Why AI literacy is now a core competency in education. Available online at: https://www.weforum.org/stories/2025/05/why-ai-literacy-is-now-a-core-competency-in-education/ (accessed May 5, 2025).

Google Scholar