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Using Different Error Handling Strategies to Facilitate Older Users' Interaction With Chatbots in Learning

Weijane Lin1, Hong-Chun Chen1, Hsiu-Ping Yueh2

  • 1Department of Library and Information Science, National Taiwan University, Taipei, Taiwan.

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Multiple error handling strategies improve voice chatbot effectiveness and satisfaction for older adults learning information and communication technologies (ICTs). This enhances their attitude toward technology and aids gerontechnology design.

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Area of Science:

  • Gerontology
  • Human-Computer Interaction
  • Information Science

Background:

  • Libraries support older adults' information literacy with user-friendly interfaces.
  • Voice User Interfaces (VUIs) are increasingly researched but frustrate older users.
  • Designing senior-friendly chatbots requires understanding user interaction challenges.

Purpose of the Study:

  • To investigate the impact of different error handling strategies on older adults' VUI chatbot interactions.
  • To inform the design of automated dialog mechanisms for senior-friendly chatbots.
  • To enhance older adults' accessibility and learning of information and communication technologies (ICTs).

Main Methods:

  • A between-subject user experiment involving 30 older adults divided into three groups.
  • Analysis of participants' behavioral patterns, performance, and interaction tactics.
  • Comparison of interactions with voice chatbots featuring distinct error handling strategies.

Main Results:

  • The use of multiple error handling strategies significantly benefits older users.
  • Effectiveness and satisfaction in human-robot interactions were enhanced.
  • Older users' attitudes toward information technology were positively facilitated.

Conclusions:

  • Multiple error handling strategies are crucial for effective and satisfying VUI chatbot use by older adults.
  • Findings provide empirical evidence for gerontechnology design, particularly in educational and living applications.
  • This research expands voice chatbot studies by examining conversation errors in human-robot interactions for older populations.