TY - JOUR
T1 - Lilobot
T2 - A Cognitive Conversational Agent to Train Counsellors at Children's Helplines : Design and Initial Evaluation
AU - Grundmann, Sharon
AU - Al Owayyed, Mohammed
AU - Bruijnes, Merijn
AU - Vroonhof, Ellen
AU - Brinkman, Willem Paul
N1 - Publisher Copyright:
© 2025. The Author(s).
PY - 2025/1/14
Y1 - 2025/1/14
N2 - To equip new counsellors at a Dutch child helpline with the needed counselling skills, the helpline uses role-playing, a form of learning through simulation in which one counsellor-in-training portrays a child seeking help and the other portrays a counsellor. However, this process is time-intensive and logistically challenging-issues that a conversational agent could help address. In this paper, we propose an initial design for a computer agent that acts as a child help-seeker to be used in a role-play setting. Our agent, Lilobot, is based on a Belief-Desire-Intention (BDI) model to simulate the reasoning process of a child who is being bullied at school. Through interaction with Lilobot, counsellors-in-training can practise the Five Phase Model, a conversation strategy that underpins the helpline's counselling principle of keeping conversations child-centred. We compared a training session with Lilobot to a text-based training, inviting experienced counsellors from the Dutch child helpline to participate in both sessions. We conducted pre- and post-measurement comparisons for both training sessions. Contrary to our expectations, the results show a decrease in counselling self-efficacy at post-measurement, particularly in Lilobot's condition. Still, the counsellors' qualitative feedback indicated that, with further development and refinements, they believed Lilobot could potentially serve as a useful supplementary tool for training new helpline counsellors. Our work also highlights three future research directions for training simulators in this domain: integrating emotions into the model, providing guided feedback to the counsellor, and incorporating Large Language Models (LLMs) into the conversations.
AB - To equip new counsellors at a Dutch child helpline with the needed counselling skills, the helpline uses role-playing, a form of learning through simulation in which one counsellor-in-training portrays a child seeking help and the other portrays a counsellor. However, this process is time-intensive and logistically challenging-issues that a conversational agent could help address. In this paper, we propose an initial design for a computer agent that acts as a child help-seeker to be used in a role-play setting. Our agent, Lilobot, is based on a Belief-Desire-Intention (BDI) model to simulate the reasoning process of a child who is being bullied at school. Through interaction with Lilobot, counsellors-in-training can practise the Five Phase Model, a conversation strategy that underpins the helpline's counselling principle of keeping conversations child-centred. We compared a training session with Lilobot to a text-based training, inviting experienced counsellors from the Dutch child helpline to participate in both sessions. We conducted pre- and post-measurement comparisons for both training sessions. Contrary to our expectations, the results show a decrease in counselling self-efficacy at post-measurement, particularly in Lilobot's condition. Still, the counsellors' qualitative feedback indicated that, with further development and refinements, they believed Lilobot could potentially serve as a useful supplementary tool for training new helpline counsellors. Our work also highlights three future research directions for training simulators in this domain: integrating emotions into the model, providing guided feedback to the counsellor, and incorporating Large Language Models (LLMs) into the conversations.
KW - BDI
KW - Chatbot
KW - Child counselling
KW - Conversational agent
KW - Education
KW - Training
UR - http://www.scopus.com/inward/record.url?scp=85215615537&partnerID=8YFLogxK
U2 - 10.1007/s10916-024-02121-8
DO - 10.1007/s10916-024-02121-8
M3 - Article
C2 - 39806193
AN - SCOPUS:85215615537
SN - 0148-5598
VL - 49
JO - Journal of Medical Systems
JF - Journal of Medical Systems
IS - 1
M1 - 5
ER -