The Future of Innovation is Interdisciplinary and AI-Enhanced
Artificial intelligence is rapidly transforming professional work, yet organizations struggle to move beyond individual productivity gains to realize AI’s full collaborative potential. Meanwhile, the perennial challenge of fostering productive interdisciplinary innovation remains as difficult as ever. In the Student Innovation Center at Iowa State University, we’ve discovered these two challenges might actually solve each other.
Why Organizations Need Interdisciplinary, AI-Enhanced Innovators
Interdisciplinary innovation has long been recognized as crucial for addressing complex “wicked problems” that resist single-discipline solutions. Yet, it has become equally important for everyday creative collaboration in contemporary organizations where teams increasingly span multiple areas of expertise.
Meanwhile, generative AI has made sophisticated technologies accessible to nearly every professional. Yet research shows a troubling pattern: while adoption is widespread, applications remain predominantly individual and task-based. As Brachman et al. (2025) note, “many existing generative AI tools for knowledge work are not inherently collaborative,” despite the fact that knowledge work itself is “often complex and collaborative.” Their research indicates that workers across various roles already use these tools for document generation, ideas, learning, and writing improvement, but more integrated collaborative applications remain underdeveloped where the potential impact is greatest.
To support innovation, scholars have championed the development of “T-shaped” professionals – individuals with deep disciplinary expertise (the vertical bar of the T) combined with the ability to collaborate across disciplines (the horizontal bar). Hansen and Bertel (2023) explain this distinction between “vertical knowledge” covering discipline-specific expertise and “horizontal knowledge” from other fields that one can understand and access. This model has proven effective for innovation challenges that require both specialized knowledge and broad perspective.
A study by Dell’Acqua et al. (2025) found that teams with AI were 3 times more likely to produce solutions in the top 10% as compared with control conditions.
Recent research on AI-assisted teamwork reveals something remarkable: AI can actually enhance and accelerate T-shaped capabilities. A study by Dell’Acqua and colleagues found that individuals with AI can perform comparably to traditional teams, while teams with AI outperform everyone else. Their research showed that “teams with AI were 9.2 percentage points more likely to produce solutions in the top decile compared to the control mean of 5.8%, that corresponds to around 3 times more chances of being in the top decile of solutions” (Dell’Acqua et al., 2025). The key factor? AI helps supplement missing expertise and promotes more balanced approaches to problems.
This emerging research suggests that AI can help technical innovators produce more user-centered, commercially viable solutions, while helping user-centered innovators contribute to more technically feasible innovations. By leveraging AI, participants effectively expanded their problem-solving horizons, demonstrating AI’s potential to foster more holistic and interdisciplinary thinking.
How We Designed an Experiential Learning Laboratory
To address this opportunity, we created the Applied AI Challenge – a structured innovation experience designed to foster interdisciplinary collaboration using AI technologies. Modeled on design sprints and hackathons, our two-week competition was hosted by the Student Innovation Center at Iowa State University. It brought together 30 students from across our university – spanning engineering, art, business, information sciences, education, and more – to develop AI solutions to real-world problems.
Our design challenge integrated four key components:
Case-based inspiration architecture: Themed “Inspiration Stations” facilitated opportunity discovery and idea generation across diverse problem domains
Applied AI literacy development: Scaffolded training in both general-purpose and specialized tools bridged technical and communication perspectives
Structured design thinking processes: Guided workflows balanced problem understanding with solution development
AI-enhanced collaboration tools: Strategic AI integration positioned these tools as complementary resources for team creativity—for example, we created a customized generative AI chatbot, the Applied AI Challenge - Project Assistant, to provide on-demand, interactive access to the competition facilitation and training materials (see figure below)
Teams self-organized around challenges that aligned with their interests and expertise, resulting in diverse project foci including assistive technologies, educational tools, health applications, and sustainability solutions. You learn more details about all the team projects via this news story: Applied AI Challenge Showcases Student Innovation.
What We Learned
Democratize Innovation by Empowering Domain Experts with AI Tools
The emergence of robust no-code and low-code AI platforms is leveling the playing field, enabling non-technical professionals to leverage their deep domain expertise in creating powerful solutions. One standout project came from a team of education and humanities students who created a navigation-focused chatbot that transforms complex university handbook policies into accessible, conversational information. Despite having limited technical backgrounds, these students used low-code tools to build a conversational agent that addressed a genuine pain point they had experienced: the difficulty of finding specific policy information buried in dense administrative documents.
Similarly, a team focused on social impact created an adaptive clothing recommendation platform connecting families of children with disabilities to appropriate apparel options. By leveraging their understanding of accessibility challenges and ability to build a relevant knowledge base, they developed a low-code recommendation system that could meaningfully improve daily life for families navigating specialized clothing needs.
The lesson? Organizations that provide domain experts with AI skill development and opportunities to create applications unlock an exciting new avenue for innovation. Rather than making technical expertise the gatekeeper, no-code and low-code AI tools empower those closest to the problems to develop solutions directly.
Enhance Technical Teams with Human-Centered Innovation Processes
On the other end of the spectrum, our AI-enhanced innovation process helped technical teams develop solutions with greater human impact. A team of computer science and engineering students developed a sophisticated assistive technology system for visually impaired users. What distinguished their approach was the emphasis on environmental awareness and navigation support rather than solely technical showmanship. Using computer vision and multimodal AI, they created a solution deeply rooted in accessibility needs.
AI can help technical innovators produce more user-centered solutions while helping non-technical innovators contribute to more technically feasible innovations.
Another technically skilled team built a stress management platform that analyzes personal data to provide personalized wellness recommendations. This code-based analytics system addressed the genuine need for personalized wellness strategies by leveraging advanced AI capabilities, demonstrating how technical expertise can be directed toward human-centered outcomes.
The takeaway? Providing technical experts and R&D teams with AI-enhanced innovation processes can lead to more breakthrough solutions. By structuring the innovation journey to emphasize human needs while leveraging AI for ideation and development, organizations can maximize the impact of their technical talent.
Build Psychological Resources That Enable Long-Term Innovation
The impact of the Applied AI Challenge went beyond the specific projects created. Our assessment revealed significant growth in participants’ innovation capacities – the psychological resources that enable creative problem-solving across contexts.
Our challenge resulted in a 12% increase in creative self-efficacy and an 8% gain in psychological capital – direct indicators of enhanced innovation capacity.
Our study focused on two psychological resources critical for innovation:
Creative Self-Efficacy: An individual’s belief in their ability to produce creative outcomes. Based on Bandura’s social cognitive theory, research shows increases in this confidence directly correspond to improvements in creative performance.
Psychological Capital (PsyCap): A developable state that combines hope (goal-directed thinking), optimism (positive expectancy), resilience (capacity to bounce back from challenges), and efficacy (confidence in task completion). Together, these resources provide the psychological foundation for innovative behavior.
Using validated pre/post test measures, we found a statistically significant 12% increase in creative self-efficacy among participants (p=.001). We also observed an 8% overall increase in psychological capital, with particularly notable gains in hope (+0.45) and optimism (+0.32) subfactors, suggesting enhanced capacity for goal-directed thinking and positive expectancy.
The takeaway? Providing experiential learning opportunities builds the confidence students and professionals need to become more creative and more likely to produce innovative outcomes. These psychological resources don’t just benefit immediate projects – they create lasting capacity for addressing future challenges across contexts.
What’s Next?
This work represents just the beginning of our exploration into how organizations can foster applied AI innovation. The challenge showed that even brief, structured experiences can significantly enhance innovation capacities while promoting human-centered approaches to AI.
Even brief, structured experiences can significantly enhance innovation capacities while promoting human-centered approaches to AI.
As we look to the future, we’re excited to expand these opportunities and continue refining our approach to interdisciplinary innovation in the AI era. After all, the most effective way to prepare for an AI-enriched future isn’t just to learn about the technology – it’s to practice creative collaboration with it.
The Applied AI Challenge and research study has been generously supported by the Jonathan Wickert Professorship in Innovation awarded to Dr. Abram Anders.
References
Abbas, M., & Raja, U. (2015). Impact of psychological capital on innovative performance and job stress. Canadian Journal of Administrative Sciences, 32(2), 128–138. https://doi.org/10.1002/cjas.1314
Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman.
Brachman, M., El-Ashry, A., Dugan, C., & Geyer, W. (2025). Current and Future Use of Large Language Models for Knowledge Work (No. arXiv:2503.16774). arXiv. https://doi.org/10.48550/arXiv.2503.16774
Dell’Acqua, F., Ayoubi, C., Lifshitz-Assaf, H., Sadun, R., Mollick, E. R., Mollick, L., Han, Y., Goldman, J., Nair, H., Taub, S., & Lakhani, K. R. (2025). The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise. SSRN. https://doi.org/10.2139/ssrn.5188231
Hansen, S., & Bertel, L. B. (2023). Becoming a Creative Genius: How a Creative Learning Environment Can Facilitate Transdisciplinary Engagement and Creative Mindsets in a Life-Long Learning Perspective. Journal of Problem Based Learning in Higher Education, 11(2), 34–53. https://doi.org/10.54337/ojs.jpblhe.v11i2.7781
Luthans, F., Avolio, B. J., Avey, J. B., & Norman, S. M. (2007). Positive psychological capital: Measurement and relationship with performance and satisfaction. Personnel Psychology, 60(3), 541–572. https://doi.org/10.1111/j.1744-6570.2007.00083.x
Mathisen, G. E., & Bronnick, K. S. (2009). Creative self-efficacy: An intervention study. International Journal of Educational Research, 48(1), 21–29. https://doi.org/10.1016/j.ijer.2009.02.009
Tierney, P., & Farmer, S. M. (2011). Creative self-efficacy development and creative performance over time. Journal of Applied Psychology, 96(2), 277–293. https://doi.org/10.1037/a0020952
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