De Kindertelefoon is a children's helpline aimed at providing (pre-)adolescents with a person to talk to for a variety of subjects such as bullying, sex, and abuse. These people who talk for De Kindertelefoon need proper training and guidance. Among the tools that De Kindertelefoon provides is the 5-phase model, a conversational model. Researchers set up a simulation of a virtual child and De Kindertelefoon to help teach the 5-phase model. However, simulation alone was not enough, and we wanted to see if a feedback system could improve the results, with immediate feedback being the focus.
Immediate feedback is when the user fills in an answer and the system immediately provides information to strengthen knowledge.
When applied to an academic environment, both immediate feedback and student engagement have been proven to be important for completing a task.

Using a literature review, we found that immediate feedback can be directly linked to educational techniques such as self-improvement, self-efficacy, and the Self-Determination Theory. Through a focus group, we also found that constructive immediate feedback is an important pillar of De Kindertelefoon.

The design was achieved by looking at the existing and limited 5-phase model as a graph and trying to find an optimal path through that graph.

After conducting a within-subjects study experiment with 34 participants, the results were inconclusive, with neither condition appearing to be more useful for the group of participants, nor either condition being better at teaching them the 5-phase model.
As the results were inconclusive, the data was explored more by looking at it as a between-subject study, which showed that the explanation sheet might perform better for knowledge.

The research shows the possible strength of feedback in a practical manner. From the results, the immediate feedback is neither more nor less resonant than a classical approach to teaching the 5-phase model.

Chatbots are tools that can potentially be utilized in chat-based child helpline training. In this type of training, the quality of the feedback received is of vital importance. This paper aims to analyze the automated feedback generated by such a chatbot. The domains analyzed include user comprehension, usefulness, and potential improvement points. In a user study, a formative assessment and two interviews were conducted for each domain, respectively.
For comprehensibility, all participants could easily understand the feedback report. They found that the bot could be easier to work around after reading the feedback, with the table being of much guidance. They found the transcript to be a welcome addition, but missing constructive feedback. Regarding improvement points, two of them were tightly related to the limitations of the chatbot, rather than the report itself. Extra guidance and instructions were deemed necessary by the participants, alongside an easier-to-read transcript interface.

Agent-based social skills training systems have been gaining attention for their potential to improve social skills development in various contexts. Through a rapid review methodology, data was collected from diverse sources, including company websites and research papers. This study then uses the collected data to categorize 8 commercial systems based on their agent model and feedback approaches, into two categorization tables. The findings reveal notable trends in the use of choice-based input, scenario-defined decision-making, and post-interaction feedback. Additionally, the paper discusses the limitations of these findings, highlights characteristics of commercial systems and compares them to research systems, as well as suggesting areas for future research. This study contributes to the understanding and advancement of agent-based social skills training systems, offering guidance to researchers in this field.

The aim of this research is to evaluate the believability of Lilobot, a conversational agent meant to act as a virtual child for training helpline workers. Numerous aspects of believability are explored by means of a user study involving a questionnaire and interview with 10 participants. Questionnaire results indicate that improvement to the chatbot's believability is likely necessary.
The findings from the interviews are that the use of emoticons and acknowledging the context of the application raise believability, while unresponsiveness and repeated utterances lower it. While Lilobot did express valid and real emotions, study participants suggested improving the appropriateness of its reactions and expanding its vocabulary.

Helpline counselors can be trained faster using agent-based social skills training systems. These systems utilize conversational agents that simulate interactions with users and provide feedback. This research evaluates the noticeability of behavior changes in such a conversational agent. By examining participants' ability to detect and interpret these changes, valuable insights can be gained to enhance the agent's representation of real-life scenarios and improve its effectiveness as a training tool.

A mixed-methods approach was employed, combining quantitative data using descriptive statistics and qualitative data through content analysis. The quantitative analysis revealed a precision score of 93% and a recall score of 36%, indicating a higher accuracy in correctly identifying behavior changes compared to capturing all instances of change. The qualitative analysis revealed that participants' perceptions fell within four distinct categories: communication style, positive emotion, negative emotion, and tone & attitude. Participants demonstrated heightened attentiveness to changes in positive behavior, particularly instances of happiness expressed by the agent, and displayed less attention towards changes in negative behavior.

In conclusion, this research demonstrates that behavior changes in the conversational agent are noticeable, albeit with varying degrees of attention across different types of behavior. Further exploration with a larger sample size is warranted to ascertain the extent of noticeability. The diverse range of behavior changes indicates the agent's capability to adapt and exhibit visible shifts in behavior. These insights can inform the refinement and development of agent-based training systems, ultimately enhancing the training experiences of chat-based helpline counselors.

Child helplines play a crucial role in delivering expert assistance to young clients facing challenges and seeking support. While counselling is instrumental in enhancing children’s mental well-being, the limited number of experienced counsellors is inadequate given the substantial workload. At this point, effective training to volunteer counsellors becomes essential. This thesis aims to expand upon a Belief-Desire-Intention (BDI) based chatbot, the simulated child, with the specific objective of enhancing volunteer training at child helpline organizations through the integration of emotional capabilities. Through the collaboration with professionals from a Dutch child helpline, the Kindertelefoon, we identified the predominant emotion of frustration along with its underlying triggers. Armed with this insight, we proposed an Emotion-BDI model capable of dynamically adjusting the chatbot’s displayed emotion in response to its environment. The evaluation of a prototype constructed based on this model supported our hypotheses that a chatbot equipped to express frustration possesses greater believability and emotional presence compared to the original version. Moreover, it also indicated heightened user enjoyability, engagement, and perceived usefulness. Within this thesis, we highlighted the enhanced feasibility of the simulated child brought about by our proposed Emotion-BDI model.

Scenario-based learning uses interactive scenarios to present the user with situations that need user input to be resolved in order to teach the user the correct behaviour. Traditionally these scenarios would be presented in a rigid order that is linearly increasing in difficulty. Every student however has a different learning rate. Studies have shown that students can lose motivation when challenged too much or too little. This project aims to improve learning efficiency and user satisfaction by adapting the scenario content to the user's skill and knowledge level.
A driving instructor application was developed where users are presented with short interactive scenarios in a 3D environment where they take control of the vehicle using a steering wheel and pedals and learn to make correct decisions when driving. For the research experiment the sessions were divided into two different modes, linear or adaptive. In the linear mode there was a strict gradual increase in difficulty. In the adaptive mode, the user's performance in the previous scenarios determined which scenario is presented next. The collected on user performance and self-efficacy showed no significant difference in user performance. There was however a significant increasement in self-efficacy when users played the adaptive session. This validates the positive value that personalization could bring to training applications.