I study how Creative Problem Solving functions in AI-augmented environments where generative systems contribute across clarification, ideation, development, and implementation. My work focuses on how human judgment, responsibility, and creative direction operate under generative abundance, with emphasis on preserving human involvement across all phases to maintain creative capacity.

Generative AI can produce large volumes of variations and ideas, altering the conditions under which problems are solved. As a result, problem framing, constraint setting, iterative development, and implementation decisions become more visible and consequential. I examine how individuals engage these processes while maintaining standards of quality, responsibility, and purpose, without relinquishing their role in generating and shaping ideas.

I study whether these shifts develop creative capacity or erode it, particularly when humans reduce their involvement in parts of the process. Specifically, I examine how educators and leaders support the development of judgment, criteria construction, and creative direction while sustaining active human participation across clarification, ideation, development, and implementation.

I am particularly interested in the following questions:

• What happens to creative agency when generative systems contribute across the full problem-solving process?
• How do individuals develop the capacity to generate, guide, constrain, and refine ideas in collaboration with AI systems?
• How does human judgment operate when generative systems produce options at scale?
• How is creative ownership established and sustained when both human and system contribute to outcomes?

Conceptual Framework

Creative Problem Solving provides a structured yet flexible approach to creativity involving clarification, ideation, development, and implementation. Movement among these components is iterative and guided by the deliberate alternation between divergence and convergence.

Generative AI alters the operating conditions of this structure. AI systems can expand divergence by rapidly producing large numbers of possibilities and can accelerate convergence through comparison, refinement, and recombination of alternatives (Hoßbach & Isaksen, 2025). These shifts increase the volume and speed of iteration but do not eliminate the need for human participation across all phases of the process.

This research examines how Creative Problem Solving functions under these conditions, with particular attention to how human involvement is maintained across clarification, ideation, development, and implementation when generative systems contribute continuously.

I am particularly interested in:

• How divergence and convergence operate when generative systems produce options at scale and humans remain active participants in both processes
• How criteria for judging ideas are constructed and applied when iteration becomes rapid and frictionless
• How assumptions about effort, authorship, iteration, ownership, and responsibility shift when generative systems contribute substantially to production, but not in isolation from human direction and generation

These changes extend beyond individual idea generation and reshape how decisions, learning, and responsibility are structured within broader systems.

This work is grounded in education and extends into entrepreneurship, organizational learning, and metacognition. In educational contexts, generative abundance reshapes how creative capacity, judgment, and ownership are developed while requiring sustained human engagement in the full creative process. In entrepreneurial contexts, it alters opportunity evaluation and strategic commitment. Within organizations, it challenges shared mental models and established learning disciplines. At the individual level, expertise increasingly involves metacognitive monitoring, criteria construction, and the disciplined calibration of AI-generated outputs, alongside continued human participation in generating and shaping ideas.

Featured Research (2023)

Integrating Artificial Intelligence into Creativity Education: Developing a Creative Problem-Solving Course for Higher Education

M.S. Creative Studies, SUNY Buffalo State, 2023

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Active Focus

I am undertaking an autoethnographic analysis of how prompts, constraints, and criteria shape AI-supported generation and how outputs are generated, evaluated, and selected within my own creative process.

This analysis examines:

• How I generate, guide, and refine ideas in collaboration with AI systems
• How I decide whether to keep, modify, or discard AI-generated outputs
• How access to large volumes of variations shifts the threshold for what I consider acceptable
• The conditions under which I stop searching and select a satisfactory output rather than continue generating alternatives
• How extended evaluation of multiple options produces decision fatigue and affects the quality of final selections

This work draws on the Creative Problem Solving tradition to examine how its assumptions about generation, judgment, and iteration hold or shift when generative systems are integrated into the process, while maintaining active human involvement across all phases.

About Selma

I focus on creative agency, creative direction, and human–AI co-creativity. I hold an M.S. in Creative Studies from SUNY Buffalo State. My work has been cited internationally in peer-reviewed journals, conference proceedings, and graduate research.

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Email: selma [at] selmadawani [dot] com
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