The Hybrid Intelligence Technology Acceptance Model (HI-TAM) presented in this paper offers a novel framework for training and adopting generative design (GD) assistants, facilitating co-creation between human experts and AI systems. Despite the promising outcomes of GD, such as augmented human cognition and highly creative design products, challenges remain in the perception, adoption, and sustained collaboration with AI, especially in creative design industries where personalized and specialized assistance is crucial for individual style and expression. In this two-study paper, we present a holistic hybrid intelligence (HI) approach for individual experts to train and personalize their GD assistants on-the-fly. Culminating in the HI-TAM, our contribution to human-AI interaction is 4-fold including (i) domain-specific suitability of the HI approach for real-world application design, (ii) a programmable common language that facilitates the clear communication of expert design goals to the generative algorithm, (iii) a human-centered continual training loop that seamlessly integrates AI training into the expert’s workflow, (iv) a hybrid intelligence narrative that encourages the psychological willingness to invest time and effort in training a virtual assistant. This approach facilitates individuals’ direct communication of design objectives to AI and fosters a psychologically safe environment for adopting, training, and improving AI systems without the fear of job-replacement. To demonstrate the suitability of HI-TAM, in Study 1 we surveyed 41 architectural professionals to identify the most preferred workflow scenario for an HI approach. In Study 2, we used mixed methods to empirically evaluate this approach with 8 architectural professionals, who individually co-created floor plan layouts of office buildings with a GD assistant through the lens of HI-TAM. Our results suggest that the HI-TAM enables professionals, even non-technical ones, to adopt and trust AI-enhanced co-creative tools.
From design automation to meaningful co-creation
The HI-TAM framework helps professionals learn, trust, and grow with generative design assistants — showing how reflection and co-adaptation can make AI adoption both creative and human-centered.
Short Intro
The HI-TAM (Hybrid Intelligence – Technology Adoption Model) framework was developed to explore how professionals can build reflective, trusting relationships with generative design assistants. The research, led by CHI and collaborators in architecture and design, connects traditional adoption theory with Hybrid Intelligence — emphasizing co-learning, psychological trust, and the human side of AI integration in creative practice.
The Research Challenge
Generative AI tools promise to boost creativity, but many professionals hesitate to use them long term — often due to uncertainty, lack of control, or fear of replacement. Traditional Technology Adoption Models explain how people start using new tools but not how they grow with them.
The challenge was to design a model that supports mutual learning — where humans and AI systems adapt to each other — and helps professionals integrate AI assistants as creative partners rather than substitutes.
The HI Approach
HI-TAM extends classical TAM by introducing Hybrid Intelligence principles such as reflective trust, human–AI co-learning, and feedback-driven adoption. To explore its potential, the research team conducted two studies: a survey with 41 architectural professionals to identify preferred Hybrid Intelligence workflows, and a mixed-methods co-creation study with 8 professionals, each collaborating with a generative design assistant to create office floor plans.
Culminating in HI-TAM, the work contributes to human–AI interaction in four key ways:
- Showing the suitability of the Hybrid Intelligence approach in real-world design contexts.
- Introducing a common language that bridges expert intent and algorithmic reasoning.
- Creating a human-centered training loop that integrates AI learning into professional workflows.
- Promoting a reflective mindset that helps experts invest time and trust in developing virtual assistants.
Rather than a finished operational framework, HI-TAM represents a conceptual and empirical prototype, validated within a specific architectural design context — offering a foundation for developing future training and adoption strategies.
Findings
- HI-TAM encouraged professionals to reflect more deeply on their creative process when co-creating with AI tools.
- Participants reported greater confidence and creative alignment when reflection and feedback were built into the workflow.
- Even non-technical professionals engaged meaningfully with AI systems when supported by structured reflection and trust-building exercises.
- The studies point to promising directions for long-term, human-centered AI adoption rather than immediate or universal validation.
Impact
HI-TAM offers a grounded yet adaptable way to understand how humans and AI can evolve together in creative work.It suggests that successful adoption depends less on technical capability and more on trust, reflection, and shared learning. By offering a conceptual foundation for developing future training and adoption strategies, HI-TAM encourages a more sustainable and empowering approach to intelligent design collaboration.
Keywords: Hybrid Intelligence, generative design, AI adoption, trust calibration, co-learning, reflective practice, architecture, creativity support tools