Professional Portfolio Summary
ITS4099 IT Research – Week 5 Project
This assignment examined how generative artificial intelligence can improve independent tabletop game development without reducing quality, originality, consistency, or human creative control. I developed a research question and hypothesis, reviewed twelve scholarly studies, compared evidence from game development, professional writing, customer support, software development, teamwork, and creativity, and evaluated the strengths and limitations of the available research. The project concluded that generative AI can improve efficiency when it is used selectively and its output is verified by a knowledgeable human. I also proposed a future study that would directly compare AI-assisted and non-AI-assisted development tasks. This project is relevant to my targeted IT support and service desk market because AI tools are increasingly becoming part of technical work and customer support. The research required me to evaluate evidence instead of accepting technology claims at face value, identify limitations, compare different types of studies, and determine where human verification remains necessary. Those skills directly support troubleshooting and technical decision-making. A service desk professional must gather information, determine which evidence is reliable, test possible solutions, recognize when a tool may be wrong, and document conclusions clearly. This assignment demonstrates research, critical thinking, technical writing, quality control, and responsible use of emerging technology.
Assignment Statement
The original assignment instructions were not retained with the available Week 5 submission. The original course work is reproduced below without replacing or inventing the missing assignment statement.
Original Week 5 Work
The Impact of Generative Artificial Intelligence on Independent Tabletop Game Development
ITS4099 – IT Research
Dr. A
September 2026
The Impact of Generative Artificial Intelligence on Independent Tabletop Game Development
Introduction—Statement of Purpose
Generative artificial intelligence (GenAI) has moved quickly from an experimental technology into a practical tool for writing, programming, image generation, research, brainstorming, editing, and other forms of knowledge work. That development creates a particularly important opportunity for independent tabletop game developers. A large studio can divide work among writers, artists, editors, programmers, designers, marketers, and other specialists, while an independent developer or very small team may be responsible for most or all of those roles. Developing a tabletop role-playing game can require worldbuilding, rules design, mathematical balancing, playtesting, editing, layout planning, artwork concepts, marketing, documentation, and digital support. The amount of work can become a major barrier even when the underlying game concept is strong.
The goal of this research project is to determine whether generative AI can improve the efficiency of independent tabletop game development without reducing the quality, originality, consistency, or human creative direction of the finished product. This distinction is important because speed alone is not enough to judge whether a technology improves a creative workflow. A tool that creates material quickly but introduces inaccurate information, repetitive ideas, weak writing, inconsistent rules, or derivative content may create additional work rather than reduce it. The practical question is therefore not simply whether GenAI can generate text, images, or ideas, but where it can be used effectively and where human judgment remains necessary.
This research is important because independent creators operate under different constraints than large organizations. Limited budgets and small teams make productivity improvements valuable, but originality and creative identity are also major reasons independent games can compete in the first place. Research on GenAI has already demonstrated measurable effects on productivity, professional writing, software development, teamwork, and creativity. Game-specific research also suggests that generative tools can support ideation and prototyping while creating concerns about authorship, creative dependence, and production quality (Alharthi, 2025; Begemann & Hutson, 2024). Taken together, these issues make independent tabletop development a useful setting for examining the balance between efficiency and human creative control.
Research Questions and Hypothesis
The primary research question is: To what extent can generative artificial intelligence improve the efficiency of independent tabletop game development without negatively affecting the quality of the finished product? For this project, efficiency includes reductions in development time, repetitive workload, and resource requirements. Quality includes originality, accuracy, consistency, usability, and the ability of the final game to reflect the developer's intended creative direction. A secondary question follows from the first: Which parts of the development process are most appropriate for AI assistance, and which should remain primarily human-directed?
The hypothesis is that a human-directed GenAI workflow will improve the efficiency of independent tabletop game development without significantly reducing product quality when AI is used primarily for supporting tasks rather than final creative decisions. GenAI is expected to be most useful for brainstorming, early drafting, editing assistance, organization, documentation, prototyping, and repetitive technical work. Human creators are expected to remain necessary for final rules decisions, game balance, narrative coherence, originality, artistic identity, and quality control. The literature reviewed below generally supports this hypothesis, although it also shows that the value of AI varies by task, user, and method of collaboration.
Literature Review
Generative AI in Game Development
The studies most directly related to game development show both the potential and the limits of GenAI. Alharthi (2025) used a mixed-methods design that included a survey of 42 game-design professionals and interviews with nine participants. Designers reported that generative AI could speed up ideation, prototyping, and repetitive work. At the same time, they raised concerns about originality, authorship, creative dependence, and the loss of human control. These findings are especially relevant to independent tabletop development because they come from professionals working in a creative design environment rather than from a purely technical or theoretical discussion of AI.
Begemann and Hutson (2024) examined generative AI inside a practical game-development pipeline. Their case study followed multiple generative tools through concept creation, 3D asset generation, optimization, and game-engine integration. The tools were useful in early conceptual work, but AI-generated outputs still required human technical skill before they were ready for production. This result demonstrates an important difference between generating an idea and creating a finished product. For tabletop development, the same distinction can apply to generated rules, lore, encounters, item descriptions, artwork concepts, or marketing material. AI may provide a useful starting point, but the developer still has to decide whether the output fits the game's mechanics, tone, continuity, and intended player experience.
Together, Alharthi (2025) and Begemann and Hutson (2024) support a selective rather than universal approach to AI adoption. GenAI appears strongest when it helps a developer explore possibilities, create drafts, or reduce repetitive effort. It becomes less reliable when the task requires production-ready precision or a strong understanding of the creator's larger design intent. This pattern is consistent with research in other professional settings and provides a useful foundation for deciding how independent tabletop developers should divide work between AI and human creators.
Productivity and Efficiency
One of the strongest arguments for GenAI is its ability to reduce the time required for certain forms of knowledge work. Brynjolfsson et al. (2025) examined more than 5,000 customer-support agents after the introduction of a generative AI assistant. The researchers found an average productivity increase of about 15%, with particularly strong benefits for less experienced workers. The results are important because they measure real workplace performance rather than only asking whether workers believed the technology was helpful. For a small development team, similar productivity gains could allow limited time to be redirected toward playtesting, design decisions, and other work that benefits more directly from human attention.
Noy and Zhang (2023) reached a similar conclusion through a preregistered experiment involving 453 college-educated professionals completing occupation-specific writing tasks. Participants with access to ChatGPT completed their work about 40% faster while producing output rated approximately 18% higher in quality. Tabletop development includes a large amount of writing, including rule explanations, item descriptions, setting material, summaries, marketing copy, and documentation. The study therefore provides evidence that AI assistance could reduce the time required for some writing-heavy portions of development. However, because the experiment measured professional writing rather than original game design, the findings should not be treated as proof that GenAI improves every creative task.
Cui et al. (2026) extended the productivity question into high-skilled technical work through three randomized field experiments involving 4,867 software developers at Microsoft, Accenture, and a Fortune 100 company. Access to an AI coding assistant increased completed work, with the combined estimate showing an improvement of roughly 26%. This is relevant even to tabletop projects because modern games may include websites, databases, automated character tools, virtual-tabletop support, or other digital components. The study suggests that GenAI can reduce some technical workload, but it also reinforces the importance of measuring whether faster output still satisfies quality requirements.
The productivity literature therefore supports the efficiency side of the hypothesis. GenAI can reduce time or increase output in several kinds of professional work, but the size and reliability of the benefit are not identical across tasks. An independent tabletop developer should not assume that the percentage improvements reported in customer support, writing, or software development will transfer directly to game design. Instead, these studies demonstrate that meaningful efficiency gains are possible and justify testing the technology within a tabletop-specific workflow.
Task Fit and the Jagged Technological Frontier
Dell'Acqua, McFowland, et al. (2026) provide one of the most useful frameworks for understanding why AI performs well in some areas and poorly in others. Their field research describes a 'jagged technological frontier,' in which GenAI can perform extremely well on one task while failing unexpectedly on another task that appears similarly difficult. Workers using AI on tasks inside the technology's capabilities experienced substantial improvements in speed and quality, while performance could decline when workers relied on AI for tasks outside those capabilities. This finding argues against treating AI as a single general-purpose replacement for expertise.
For tabletop development, the jagged frontier explains why success with one AI-assisted activity should not automatically justify using AI everywhere. A model that performs well when rewriting a rule for clarity may not be equally reliable when evaluating whether the rule is mathematically balanced. It may produce dozens of character concepts quickly but fail to notice that several contradict established lore. It may summarize playtest notes effectively while overlooking a subtle interaction between mechanics. The developer therefore needs a task-by-task strategy that considers both the strengths of the model and the consequences of an incorrect output.
Dell'Acqua, Ayoubi, et al. (2026) also examined AI as a teammate rather than simply as an automation tool. In a preregistered field experiment with 791 professionals at Procter & Gamble, individuals using AI could perform at levels comparable to human teams without AI on product-innovation challenges, and AI helped participants integrate technical and commercial perspectives. This result is highly relevant to independent developers because one person may be forced to work across several specialized roles. GenAI may help bridge some knowledge gaps and provide perspectives that a small team would otherwise lack, but the study does not eliminate the need for human evaluation. The final judgment about whether an idea is correct, appropriate, and useful remains a human responsibility.
Creativity, Originality, and Diversity
Productivity is only one part of successful game development. A tabletop game also needs distinctive mechanics, settings, characters, stories, and experiences. Doshi and Hauser (2024) found that access to generative AI could improve the creativity of individual stories, particularly for participants who began with lower creative ability. At the same time, stories across the group became more similar to one another. This difference between individual improvement and collective diversity is especially important for independent creators. A developer may receive a stronger idea than they would have produced alone, but widespread reliance on similar models could cause different games to move toward similar language, structures, or themes.
Ashkinaze et al. (2025) also examined how AI-generated ideas affect human creativity and idea diversity. Their large experiment with more than 800 participants from over 40 countries found that exposure to AI ideas changed the diversity and evolution of human ideas even when it did not necessarily make an individual's ideas more creative. The implication is that AI can shape the creative environment simply by influencing what people consider before they create. In tabletop development, brainstorming with GenAI may therefore change the direction of a setting or mechanic even when the final content is written by the developer.
Mei et al. (2025) add another dimension by separating output performance from the creator's experience. In an experiment with 225 university students, participants using ChatGPT produced stronger work on several measures and completed the task with less effort, but they also reported less enjoyment and placed less value on the creative experience. This matters because independent tabletop development is often both work and a creative pursuit. A workflow that produces more content but leaves the designer less connected to the project could still be undesirable. Efficiency should therefore be evaluated alongside creative ownership, engagement, and satisfaction.
Sun et al. (2025) found that GenAI could improve employee creativity in a field experiment, but the benefit was stronger among employees who used stronger metacognitive strategies. In other words, people benefited more when they actively monitored and adjusted their own thinking instead of passively accepting AI output. Zhu and Zou (2026) similarly found that access to ChatGPT improved average creative performance in an experimental setting, but the quality of human-AI interaction mattered and simple access to AI did not guarantee complementary performance. These studies support a human-directed model in which the developer questions, revises, rejects, and redirects generated suggestions instead of treating the model as an independent designer.
Human-AI Collaboration and Quality Control
Across the twelve studies, a consistent theme is that GenAI is most valuable when it expands human capability rather than replacing human judgment. The productivity studies show that AI can reduce effort and increase output, while the creativity studies show that the same assistance can influence originality, diversity, ownership, and the creative experience. The game-development studies demonstrate that generated material may work well during ideation but still require substantial refinement before it is production ready. These findings are not contradictory. Instead, they identify the conditions under which GenAI is most likely to be useful.
For an independent tabletop developer, a practical human-AI workflow would assign different levels of AI involvement to different tasks. Low-risk supporting work such as organizing notes, summarizing playtest feedback, generating alternative wording, creating draft descriptions, or producing lists of ideas can tolerate more AI involvement because the developer can review the output before it affects the final product. Higher-risk tasks such as rules balance, final narrative decisions, factual claims, intellectual-property decisions, and publication-ready content require greater human control. This approach follows the task-fit findings of Dell'Acqua, McFowland, et al. (2026) while also addressing the originality concerns identified by Alharthi (2025), Doshi and Hauser (2024), and Ashkinaze et al. (2025).
The evidence also suggests that experience and skill influence the value of AI assistance. Brynjolfsson et al. (2025), Noy and Zhang (2023), and Cui et al. (2026) all found meaningful gains among workers, with some of the strongest improvements occurring among less experienced or initially lower-performing participants. This may be valuable for independent developers who cannot afford specialists in every area. However, a less experienced user may also have greater difficulty recognizing incorrect or low-quality AI output. GenAI can therefore reduce a knowledge gap without completely replacing the expertise needed to validate the result.
Quality control becomes the central responsibility of the human developer. AI-generated content should be treated as material to evaluate rather than material that is automatically correct. Rules need to be tested, lore needs to be checked against existing material, writing needs to be revised for voice and consistency, and technical outputs need to be verified. This process preserves the efficiency benefits of GenAI while reducing the risk that speed becomes a substitute for quality. It also protects the developer's creative identity by making AI one input into the process rather than the source of final authority.
Research Methods and Strength of the Evidence
The twelve articles use several empirical approaches, which strengthens the literature review because the conclusions do not depend on a single type of evidence. Alharthi (2025) combines surveys and interviews with game-design professionals, while Begemann and Hutson (2024) use a practical case study of an AI-assisted game-development pipeline. Brynjolfsson et al. (2025), Cui et al. (2026), and both Dell'Acqua et al. (2026) studies examine GenAI in real professional environments. Noy and Zhang (2023), Doshi and Hauser (2024), Mei et al. (2025), Ashkinaze et al. (2025), Sun et al. (2025), and Zhu and Zou (2026) provide experimental evidence related to performance or creativity.
This variety is useful because independent tabletop development combines several types of work. No single customer-support, writing, software, creativity, or game-development study can answer the full research question by itself. When the findings are considered together, however, a pattern emerges: GenAI frequently improves speed or performance on appropriate tasks, but its effects depend on the user's skill, the task, and the way the technology is integrated. Creativity and quality also cannot be reduced to a single performance score. Originality, diversity, ownership, consistency, and the human experience of creating all matter.
The literature also has an important limitation. Most of the strongest experimental studies examine professional work or creative tasks outside independent tabletop development. Even the game-specific studies do not directly measure a complete tabletop role-playing game project from early concept through finished publication. Applying the broader findings to tabletop development is reasonable because many underlying activities overlap, but direct evidence is still limited. That gap provides a clear reason for a new study focused specifically on independent tabletop developers.
Conclusion
The evidence reviewed in this project generally supports the hypothesis that a human-directed GenAI workflow can improve the efficiency of independent tabletop game development without necessarily reducing quality. Productivity research demonstrates that generative AI can reduce completion time and increase output in professional writing, customer support, software development, and knowledge work (Brynjolfsson et al., 2025; Cui et al., 2026; Noy & Zhang, 2023). Game-specific studies also show practical value for ideation, prototyping, and early creative work (Alharthi, 2025; Begemann & Hutson, 2024). These findings support using GenAI to reduce repetitive workload and expand the capabilities of a small development team.
At the same time, the literature does not support unrestricted automation. Research on the jagged technological frontier demonstrates that AI performance depends heavily on task fit (Dell'Acqua, McFowland, et al., 2026). Creativity research shows that stronger individual output may be accompanied by reduced diversity, altered idea development, or a weaker sense of creative ownership (Ashkinaze et al., 2025; Doshi & Hauser, 2024; Mei et al., 2025). Sun et al. (2025) and Zhu and Zou (2026) further demonstrate that the quality of human engagement with AI influences the results. The answer to the primary research question is therefore conditional: GenAI can improve efficiency while preserving quality when the developer uses it selectively, evaluates its output, and retains control over final creative and technical decisions.
Based on the literature, a new study should directly compare AI-assisted and non-AI-assisted independent tabletop development. A practical study could recruit independent tabletop designers and assign them comparable development tasks involving rules writing, setting creation, encounter design, editing, and playtest preparation. Participants could complete some tasks without GenAI and other tasks with access to a generative AI system. Researchers could measure completion time, number of revisions, error rates, originality ratings, rule clarity, and blind evaluations of overall quality. Participants could also report their sense of creative ownership, satisfaction, and perceived workload.
The study should also separate tasks instead of producing only one overall AI-versus-no-AI score. The existing literature strongly suggests that GenAI's value changes across the technological frontier. Measuring brainstorming, drafting, rules analysis, editing, and technical support separately would identify where AI creates real benefits and where it introduces additional correction or quality-control work. A follow-up phase could have outside players blind-test the resulting game material to determine whether measurable productivity gains translate into a better player experience.
Such a study would address the largest gap identified in this review by producing direct empirical evidence about GenAI in independent tabletop development rather than relying mainly on findings transferred from other forms of work. The expected result is that AI-assisted developers would complete supporting and drafting tasks faster, while human-directed review would remain necessary to protect originality, coherence, balance, and final quality. If supported, that result would provide independent developers with a more useful framework than simply asking whether AI is good or bad for creative work. It would identify the specific parts of the workflow where GenAI can save limited time and resources while keeping the human creator at the center of the game.
References
Alharthi, S. A. (2025). Generative AI in game design: Enhancing creativity or constraining innovation? Journal of Intelligence, 13(6), 60. https://doi.org/10.3390/jintelligence13060060
Ashkinaze, J., Mendelsohn, J., Li, Q., Budak, C., & Gilbert, E. (2025). How AI ideas affect the creativity, diversity, and evolution of human ideas: Evidence from a large, dynamic experiment. In Proceedings of the ACM Collective Intelligence Conference (pp. 198–213). Association for Computing Machinery. https://doi.org/10.1145/3715928.3737481
Begemann, A., & Hutson, J. (2024). Empirical insights into AI-assisted game development: A case study on the integration of generative AI tools in creative pipelines. Metaverse, 5(2), 2568. https://doi.org/10.54517/m.v5i2.2568
Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889–942. https://doi.org/10.1093/qje/qjae044
Cui, K. Z., Demirer, M., Jaffe, S., Musolff, L., Peng, S., & Salz, T. (2026). The effects of generative AI on high-skilled work: Evidence from three field experiments with software developers. Management Science. Advance online publication. https://doi.org/10.1287/mnsc.2025.00535
Dell’Acqua, F., Ayoubi, C., Lifshitz, H., Sadun, R., Mollick, E., Mollick, L., Han, Y., Goldman, J., Nair, H., Taub, S., & Lakhani, K. R. (2026). The cybernetic teammate: A field experiment on generative AI and teamwork. Organization Science, 37(4), 1217–1242. https://doi.org/10.1287/orsc.2025.20702
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Doshi, A. R., & Hauser, O. P. (2024). Generative AI enhances individual creativity but reduces the collective diversity of novel content. Science Advances, 10(28), eadn5290. https://doi.org/10.1126/sciadv.adn5290
Mei, P., Brewis, D. N., Nwaiwu, F., Sumanathilaka, D., Alva-Manchego, F., & Demaree-Cotton, J. (2025). If ChatGPT can do it, where is my creativity? Generative AI boosts performance but diminishes experience in creative writing. Computers in Human Behavior: Artificial Humans, 4, 100140. https://doi.org/10.1016/j.chbah.2025.100140
Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh2586
Sun, S., Li, Z. A., Foo, M.-D., Zhou, J., & Lu, J. G. (2025). How and for whom using generative AI affects creativity: A field experiment. Journal of Applied Psychology, 110(12), 1561–1573. https://doi.org/10.1037/apl0001296
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