From Performers to Creators: Understanding Retired Women's Perceptions of Technology-Enhanced Dance Performance

Through co-design workshops with retired Chinese women, this paper demonstrates that age-sensitive interactive dance technologies and AI-generated content can lower technical barriers and transform these dancers from passive performers into empowered co-creators of their stage performances.

The Gist

Imagine a group of grandmothers in China who love to dance. There are over 100 million of them! They dance in parks and community centers to stay healthy, make friends, and feel happy. But when they want to put on a real show on a stage, they hit a wall.

Think of their current stage setup like a black-and-white TV trying to show a 4K movie. The dancers are full of color, emotion, and energy, but the background behind them is usually a boring, static picture (like a generic photo of a forest or a city) that doesn't move or match their dance. They want to create magical, professional-looking shows, but they don't have the money, time, or technical skills to hire a Hollywood stage designer.

This paper is about a team of researchers who asked: "How can we give these dancers the magic wand to design their own stage?"

The Problem: The "Three Locks"

The researchers found three main things stopping these women from creating amazing shows:

1. The Resource Lock: They can't afford professional lighting or video teams.
2. The Age Lock: As we get older, our bodies and memories change. Remembering complex dance moves is harder, and standing still for hours to set up tech is tiring.
3. The Tech Lock: The fancy tools used by professional dancers are too complicated. They are like trying to fix a car engine when you just want to drive the car.

The Solution: "StageTailor" (The Magic Dressmaker)

The researchers built a prototype system called StageTailor. Think of it as a smart, magical dressmaker for dance stages. Instead of needing to know computer code, the dancers just talk to it.

The system works in two simple steps:

Step 1: The Dream (AI Video Generation)

• How it works: The dancers type in a few simple words (keywords) like "sunset," "butterflies," or "Tibetan dance."
• The Magic: An AI (a super-smart computer brain) takes those words and paints a whole new video background. It's like telling a genie, "I want a scene with a golden sunset and dancing flowers," and the genie instantly creates it.
• The Twist: If the first picture isn't quite right, the dancers can just add more words to fix it. It's a conversation, not a command.

Step 2: The Dance (Interactive Effects)

• How it works: The dancers record themselves dancing. The system watches their movements.
• The Magic: If a dancer waves her hand, a digital butterfly might flutter out of her hand. If she spins, digital ribbons might swirl around her. The background isn't just a picture anymore; it's alive and reacting to them.

What Happened When They Tried It?

The researchers held two workshops with retired women to test this. Here is what they learned:

• Keywords are King: The women loved using simple words instead of writing long stories. It was like playing a game of "Guess the Picture" rather than writing an essay. It lowered the barrier so anyone could start.
• From "Actors" to "Directors": Before, these women felt like actors following a script written by someone else. With StageTailor, they became the directors. They felt a huge sense of pride saying, "I made that background. I made those effects."
• The "Uncanny Valley" of Tech: Sometimes, the AI made mistakes. It might put a butterfly flying through a tree instead of around it. The women realized that while the tech was amazing, it still needed their human touch to make it feel "right." They had to learn how to talk to the AI to get the perfect result.

The Big Takeaway

The most important thing this paper discovered isn't just about dance; it's about dignity and creativity in aging.

Usually, we think of technology as something that replaces older people or makes them feel left behind. But here, technology acted as a bridge. It didn't replace their talent; it amplified it.

• Before: They were passive performers waiting for a backdrop.
• After: They were active creators, designing their own worlds.

The Analogy of the "Digital Paintbrush"

Imagine a painter who has lost the use of their hands. They can still see beautiful colors and imagine amazing scenes, but they can't hold a brush.

• Old Tech: Telling them, "Here is a pre-made painting, just look at it."
• StageTailor: Giving them a voice-activated paintbrush. They say, "Paint a blue sky with a red sun," and the brush paints it. They still made the artistic choice; the technology just did the heavy lifting.

Conclusion

This paper shows that when we design technology with empathy (understanding the specific needs of older adults), we can turn a group of retired women from passive participants into powerful co-creators. They didn't just learn to use a computer; they learned to use AI to tell their stories, express their beauty, and take control of their stage.

It's a reminder that creativity doesn't retire; it just needs the right tools to shine again.

Technical Summary

1. Problem Statement

The paper addresses the challenges faced by over 100 million retired women in China who engage in community dance. While these groups seek to elevate their performances from simple participation to intentional artistic creation, they face three primary barriers:

• Lack of Professional Support (C1): Community stages lack professional lighting, visual designers, and technical staff, resulting in generic, static, or mismatched backdrops.
• Age-Related Decline (C2): Physical and cognitive declines (e.g., reduced flexibility, memory issues) make precise movement execution and complex choreography recall difficult, often leading to a disconnect between the dancer's intent and the performance.
• The Digital Divide (C3): Existing interactive dance technologies and AI tools are designed for professionals or require high technical literacy, making them inaccessible to non-professional older adults.

The core problem is the gap between the creative ambitions of retired women and the limited, static resources available to them, exacerbated by a lack of age-sensitive design in current interactive and generative AI systems.

2. Methodology

The research employed a two-phase co-design approach involving community-based retired women dancers:

• Workshop I (Exploratory, N=15):

  • Goal: To understand current pain points, attitudes toward interactive dance, and design requirements.
  • Activities: Participants reviewed a taxonomy of interactive dance systems, engaged with physical probes (Kinect-based motion tracking, Unity prototypes), and discussed their past performances.
  • Outcome: Identification of a tension between the desire for creative agency and the lack of technical skills. This led to three Design Considerations (DCs): (1) Low-barrier entry, (2) Embodied connection between movement and visuals, and (3) Empowerment through customization/ownership.

• System Development (StageTailor):

  • Based on DCs, the authors developed StageTailor, a two-step co-creative system:
    1. Scene Generation: Uses LLMs (Ernie Bot) to expand user keywords into rich scene descriptions, which are then converted into background videos using Text-to-Video AI (Haiper).
    2. Interactive Integration: Uses Kinect motion capture to track dancers' movements and props. Users select visual effects (e.g., butterflies, ribbons) from a library that are synchronized and composited onto the AI-generated background.

• Workshop II (Evaluation, N=16):

  • Goal: To evaluate the usability, usefulness, and experiential value of StageTailor.
  • Procedure: Participants used StageTailor to create a personalized dance background video, followed by questionnaires (Likert scale) and semi-structured focus group interviews.
  • Analysis: Mixed-methods analysis combining quantitative satisfaction scores with reflexive thematic analysis of qualitative data.

3. Key Contributions

The paper makes three primary contributions to the HCI and Aging fields:

1. Conceptual Framework: Age-Sensitive Creative AI Mediation

   • The authors propose a new design framework where AI acts not as a replacement for human creativity, but as a mediator that translates embodied, tacit knowledge (movement, rhythm, emotion) into digital outputs. This framework prioritizes redistributing aesthetic control to older adults.

2. Empirical Understanding of Constraints and Empowerment

   • The study provides empirical evidence on how participatory scaffolds (low-barrier keyword input, motion-aligned visuals) enable a pivotal shift in user roles. It demonstrates that retired women can transition from passive recipients of pre-made content to empowered co-creators when technical barriers are lowered.

3. Design Implications for Inclusive AIGC Systems

   • The paper derives eight specific design implications (DIs) for creating age-sensitive creative tools, including:
     • Treating keyword input as a reflective, iterative dialogue rather than a static command.
     • Using visual selectors (e.g., galleries of lighting/mood) to bridge vocabulary gaps.
     • Providing multiple differentiated outputs to aid reflective selection.
     • Grounding interactive effects in scene semantics (e.g., effects that interact with the background environment, not just the dancer) to ensure narrative coherence.

4. Key Results

• Satisfaction: The system achieved an average satisfaction rating of 4.07/5, significantly higher than traditional methods.
• Keyword Input: 14/16 participants preferred keyword-based input over full descriptions. It lowered cognitive load and served as a catalyst for idea refinement (average 2.81 iterations per person). However, some found AI-generated text descriptions too ornate.
• AI Video Generation: Participants were initially excited by the novelty but reported an expectation gap regarding temporal flow, emotional nuance, and stylistic consistency (avg. satisfaction 3.33/5). They struggled to translate complex mental imagery into precise text prompts.
• Interactive Effects: While interactivity was highly rated (4.0/5), the final composite video scored lower (3.0/5) due to a conceptual mismatch. Effects often felt "pasted on" rather than integrated with the AI-generated background (e.g., a butterfly flying through a static forest without interacting with trees).
• Role Transformation: The most significant finding was the psychological shift. Participants reported a strong sense of authorship and ownership, feeling they were "co-creators" rather than just performers. One participant noted, "This tool finally lets me create something that feels like mine."

5. Significance

• Redefining Aging and Creativity: The study challenges the narrative of older adults as passive technology consumers. It demonstrates that with the right scaffolding, they can engage in high-stakes, complex creative processes like stage production.
• Bridging the Digital Divide: It offers a practical model for integrating AIGC and LLMs into community practices, showing how natural language interfaces can democratize access to professional-grade tools.
• Future of Interactive Performance: The work highlights the need for context-aware AI in performance. Future systems must move beyond simple motion-triggering to semantic integration, where digital effects understand and interact with the generated narrative environment.
• Design for Agency: The paper contributes to the "Design for Agency" scholarship by showing how technical design can redistribute power, allowing marginalized groups (non-professional older women) to control their artistic expression and cultural representation.