Mentorable Interfaces for Automated Vehicles: A New Paradigm for Designing Learnable Technology for Older Adults

Title of the Paper

Mentorable Interfaces for Automated Vehicles: A New Paradigm for Designing Learnable Technology for Older Adults

Paper Information

• Authors: Togtokhtur Batbold, Alessandro Soro, Ronald Schroeter
• Conference: CHI Conference on Human Factors in Computing Systems (CHI ’24)
• Conference Date and Location: May 11–16, 2024, Honolulu, Hawaii
• DOI: 10.1145/3613904.3642390
• Keywords: Older adults, automated vehicles, learning methods, digital literacy, technology education

Research Background and Problem

• Identified Issues:

  • Older adults face challenges in learning new technologies, particularly in-vehicle interfaces for automated vehicles, due to diverse learning styles and low self-confidence, which impact technology adoption rates.
  • Current learning design approaches (e.g., "learnability") primarily focus on independent trial-and-error processes, which are unsuitable for older users who require more support.
  • The proliferation of automated vehicles (AVs) demands a high level of digital literacy from older users, who may lack adequate training and support.

• Significance:

  • With the global trend of population aging, ensuring older adults’ ability to use technology and maintain mobility can significantly enhance their quality of life and social participation.
  • Without learning opportunities, the potential benefits of automated vehicles for older users may remain unrealized.

• Research Motivation:

  • Traditional learning theories and design methods fail to adequately address the needs of older adults, necessitating the exploration of new paradigms that incorporate social support and interactive learning.
  • This study introduces the concept of "mentorability," which emphasizes external support and guidance to optimize the learning experience of older users with technology.

Solution

• Proposed Approach:

  • A new framework called "mentorability" is introduced, highlighting the importance of a mentor role in designing learnable technologies.
  • Through semi-structured interviews with 8 older adults aged 60 to 81, the study identifies their learning preferences and challenges, leading to the design of a conceptual framework that supports older users in learning in-vehicle interfaces.

• Innovative Aspects:

  • Expands the principle of "learnability" into "mentorability" to better reflect the learning needs of older users.
  • Develops a multidimensional model integrating social interaction, support networks, and psychological motivation to guide future technology design.
  • Advocates for a mentor-student relationship (rather than independent learning processes) to enhance older adults’ ability to use technology.

• Implementation Steps and Key Techniques:

  1. Conduct contextual inquiry interviews in participants’ personal vehicles to analyze their interaction habits and learning experiences.
  2. Use coding and thematic analysis (e.g., affinity diagramming) to extract key themes from the interviews.
  3. Introduce psychological and technical support modules to design interfaces tailored to older adults’ learning styles.

Research Findings

• Specific Findings:

  • Identified several preferences of older adults when learning vehicle interfaces:
    • A stronger preference for guided learning (e.g., direct demonstrations by a mentor) over spontaneous trial-and-error processes.
    • A preference for verbal explanations and multimodal (visual and auditory) information delivery methods.
    • A need for reliable sources of information (e.g., official dealerships or built-in system tutorials).
  • Proposed the "mentorability" framework, encompassing three core components:
    1. Evolving Mentor Role: Emphasizes the mentor’s role in assessing learners’ experiences and abilities and providing need-based support.
    2. Psychological Support: Addresses low confidence and internalized age-related biases associated with learning new technologies.
    3. Technical Support: Designs demonstration and explanation modes tailored to older adults’ needs for evolving technologies.

• Comparison with Existing Methods and Advantages:

  • Compared to traditional "learnability," "mentorability" integrates technological and social support, aligning with older adults’ social and collaborative learning preferences.
  • The dynamic mentor-learner relationship significantly reduces frustration during the initial stages of technology adoption.

• Experimental or Evaluation Results:

  • Based on interview data, the study demonstrates that interactive learning designs (e.g., real-time guidance or multimodal support) help older users learn technology more quickly and effectively.
  • Older users can overcome technological barriers and build learning confidence through interactions with peers or trusted mentors.

• Limitations and Future Directions:

  • Limitations:
    • Small sample size, limited to urban/suburban older populations, without fully covering older adults in remote areas.
    • Experiments were conducted in static interview settings rather than during actual driving.
  • Future Directions:
    • Expand research to more diverse regions and backgrounds, particularly rural areas.
    • Conduct naturalistic experiments in real driving environments.
    • Explore the application of virtual mentors (e.g., through augmented or mixed reality technologies).

Conclusion

This study identifies the learning preferences and barriers of older users and proposes the "mentorability" framework, emphasizing the value of mentors in designing learning-friendly technologies. The framework not only offers a new perspective for designing in-vehicle interfaces for automated vehicles but also provides guidance for optimizing other technologies aimed at older users.