# Work-in-Progress: Exploring Generative AI in Social Robots for Children with Autism Spectrum Disorder

Inès Allag, Armand Manukyan, Jérôme Dinet, Marie Rychalski

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Inès Allag, Armand Manukyan, Jérôme Dinet, Marie Rychalski. Work-in-Progress: Exploring Generative AI in Social Robots for Children with Autism Spectrum Disorder. ECCE '25: Proceedings of the 36th Annual Conference, European Association of Cognitive Ergonomics (EACE), Oct 2025, Tallin, Estonia. pp.1 - 4, 10.1145/3746175.3746205. hal-05302487

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# Work-in-Progress: Exploring Generative AI in Social Robots for Children with Autism Spectrum Disorder

### Inès Allag

University of Lorraine
Nancy, France
ines.allag6@etu.univ-lorraine.fr

### Jérôme Dinet

University of Lorraine
Nancy, France
jerome.dinet@univ-lorraine.fr

## Abstract

Autism spectrum disorder (ASD) presents complex challenges in social communication, emotional understanding, and adaptability to dynamic environments. Social robots have emerged as promising tools to support children with ASD by providing structured, predictable, and engaging interactions. Recent advances in generative artificial intelligence (AI) offer new opportunities to enhance the adaptability and responsiveness of these robots, enabling more naturalistic and personalized exchanges. This study investigates the integration of generative AI, specifically ChatGPT-powered conversational modules, into two social robots, Nao and Buddy, to evaluate their impact on the social communication skills, reasoning abilities, and educational engagement of children with ASD. Following a prospective design, interviews with educators identified the needs and objectives pertinent to the children. These insights informed the development of contextualized prompts and interaction scenarios. Experimental pedagogical sessions will be conducted in classroom settings with fifty children aged 8 to 14 years diagnosed with ASD. Qualitative observations and quantitative assessments will be employed to analyze engagement levels, communicative behaviors, and problem-solving skills during child-robot interactions. Preliminary findings aim to assess how AI-enhanced robots can foster greater conversational engagement, improved social reciprocity, and increased motivation for educational tasks. The study underscores the potential of combining social robotics with generative AI to support the autonomy and inclusion of neurodivergent learners. Future work will focus on adaptive algorithm development, longitudinal impact assessment, and scalability across diverse educational contexts.

## CCS Concepts

* Computing methodologies → *Cognitive science*.

## Keywords

autism, artificial, intelligence, robotic, educationnal, children

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This work is licensed under a Creative Commons Attribution 4.0 International License.
ECCE 2025, Tallinn, Estonia

© 2025 Copyright held by the owner/author(s).
ACM ISBN 979-8-4007-2033-8/25/10
https://doi.org/10.1145/3746175.3746205

### Armand Manukyan*

University of Lorraine
Nancy, France
armand.manukyan@univ-lorraine.fr

### Marie Rychalski

University of Lorraine
Nancy, France
marie.rychalski@univ-lorraine.fr

## ACM Reference Format:

Inès Allag, Armand Manukyan, Jérôme Dinet, and Marie Rychalski. 2025. Work-in-Progress: Exploring Generative AI in Social Robots for Children with Autism Spectrum Disorder. In *36th Annual Conference of the European Association of Cognitive Ergonomics (EACE) (ECCE 2025), October 07–10, 2025, Tallinn, Estonia*. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3746175.3746205

# 1 Introduction

## 1.1 Autism spectrum disorder and social robotics

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by communication deficits and restricted and/or repetitive behaviors [1]. Each individual with ASD is different and may have symptoms of greater or lesser intensity. Its understanding has evolved considerably over time, leading to the concept of a spectrum in order to better account for interindividual differences [27]. Many difficulties such as nonverbal communication, understanding and expressing emotions, or managing unexpected events can become barriers that complicate social interactions and daily life [1]. In this context, the rise of new technologies offers new opportunities to address these challenges. Robotics, in particular, is a promising area for innovation to benefit children with ASD. Traditional perspectives have often framed these challenges as deficits inherent to autistic individuals. However, the "double empathy problem," a theory proposed by Milton [2012], posits that these difficulties arise from a mutual lack of understanding between autistic and non-autistic individuals, rather than a one-sided deficit [16]. This bidirectional empathy gap suggests that social communications are a product of differing cognitive and perceptual experiences, emphasizing the need for interventions that bridge this divide [16].

For children with ASD, interacting with a social robot is often easier than interacting with humans. Indeed, robots can be used to create a structured and predictable environment that can be more reassuring for children. [13, 19]. In addition, communication barriers such as behavioral variations, microexpressions, and intonations are not present in child-robot interactions [7, 11, 13]. This can help children better understand their interlocutor and feel more comfortable interacting with them. Moreover, this can reduce social difficulties and cognitive overload associated with human interactions. Studies have shown that children are more
engaged and express social behaviors toward robots [19]. These new forms of interaction also improve key social components such as eye contact, joint attention, and language production [13, 19]. In some studies, robots even help redirect children's attention toward their peers, promoting cooperative behavior and social interaction [8]. Social robots are therefore educational and therapeutic tools with real potential for children with ASD [11]. There are many types of social robots, humanoid or otherwise, that can be used in different ways, such as social mediators or learning assistants [11]. Robots have also been shown to influence interactions differently depending on their degree of complexity and human resemblance [19]. Kumazaki points out that the effectiveness of an intervention may depend on the compatibility between the robot and the child [13].

## 1.2 Generative artificial intelligence - Evolution and prospects

Artificial intelligence (AI), first seen as a new technology that lets machines perform tasks associated with human intelligence - such as problem solving or decision making - has been growing since it first emerged in the 1950s [15]. Its design and applications have evolved considerably over time from task automation to predictive models. There is therefore no universal way to define it. Russell and Norvig also refer to agents able to act rationally in complex environments [21]. Today, AI is now integrated into our daily lives and many areas of activity, including the health [6] and education[5] sectors. Traditional AI can learn and adapt from new information and experience. But now, generative AI is able to generate new content such as text and images and engage in more natural conversations with humans[17]. The emergence of this new this new branch of AI has expanded the field of possibilities and offers new, innovative, and promising applications for new technologies.

This represents real potential to be explored for use with people with disabilities. Traditional AI is already widely used in assistive technology, with applications such as voice assistance, natural language processing (NLP) and communication assistance technologies [2, 14]. In their study on the use of chatbots for the conversational skills of children with ASD, Gu et al. highlight the limitations of current systems, which lack adaptability, and emphasize the potential of generative models that can adapt to specific user needs and generate more natural and contextual conversations [10]. Indeed, rather than reacting to existing data, generative AI can now create more rich and personalized content by adapting to context and users [9, 10, 14, 26]. People with disabilities face many challenges in their daily lives, such as planning, social communication, and understanding complex environments. Generative AI can therefore act as a personalized assistance tool to enable greater social inclusion and autonomy [24]. Studies are also exploring its use and potential in education [4] and in special education. Generative AI can indeed be used in these contexts to personalize learning and reduce learning barriers for children with cognitive disabilities [9, 14].

## 1.3 Enhance social robots with generative AI

In recent years, social robots have emerged as promising tools to support individuals with ASD. These robots offer consistent,

predictable interactions, which can be less overwhelming than human interactions for many autistic individuals [20]. Studies have demonstrated that interactions with social robots can enhance social behaviors, including eye contact, joint attention, and verbal communication [12]. Moreover, the adaptability of robots allows for personalized interventions that cater to the unique needs of each individual [3]. The possibilities offered by generative AI become even more interesting when they can be combined with the advantages of social robots mentioned earlier. This marks a paradigm shift in human-robot interaction (HRI), moving from robots with scripted behaviors to intelligent robots able to interact and adapt to their interlocutors in real time, giving the illusion of conversing like humans [17]. The concept of anthropomorphism—the attribution of human characteristics to non-human entities— was already very present in HRI [25] and may be a determining factor in the acceptability of robots [7, 17, 25]. Generative AI and its ability to imitate humans therefore accentuates the degree of anthropomorphism. On the one hand, it can improve the acceptability of robots but, on the other hand, also destabilize users by blurring the line between real and virtual conversation [17].

Social robots enhanced with generative AI open up new areas of research for their application in assisting people with disabilities and in education. A few studies are beginning to show their potential with people with ASD, particularly with the possibility of personalized education through individualized learning [23] but also for improving social skills through adaptive interactions [2]. The combination of social robots and generative AI therefore shows promise for education [7] and special needs teaching [2, 22]. The integration of generative AI into social robots represents a significant advancement in creating more naturalistic and adaptive interactions. Generative AI enables robots to produce contextually appropriate responses, facilitating more engaging and meaningful exchanges [18]. This technological synergy holds the potential to further bridge the empathy gap identified in the double empathy problem by allowing robots to better interpret and respond to the nuanced communication styles of autistic individuals.

## 2 Method

The aim of this study is to evaluate the comparative benefits of two robots, Nao and Buddy, with two conversational modules implemented and powered by ChatGpt, with children with autism and/or intellectual disabilities. More specifically, the aim is to assess how the conversational agents integrated into the robots enable children to improve their social skills, their reasoning and their motivation to carry out an educational task. Initially, a prospective study using semi-directive interviews will be carried out with specialist teachers and educators, to gather information on the needs and objectives identified in the children being supported. A clinical analysis of the tasks performed and the social scenarios proposed will be carried out to characterize the difficulties and potential levers for development. This information will serve as a starting point for setting up the initial prompts given to the conversation modules. Since the quality and relevance of interactions depend heavily on the quality of the instructions given to the AI as input, prompts must be constructed with context and detailed requests focused on the child's needs. It will also be specified that the AI must adapt and evolve its
speech based on the child's verbal responses. The activities created based on these prompts will be tested beforehand in order to make any necessary adjustments and modifications before trying them out with the children. Secondly, experimental pedagogical situations will be conducted in a classroom environment. In the presence of the teacher, the children will question the robot with AI, on a given subject. Clinical observation, using qualitative observation grids, will enable us to study the discursive thread between the robot, the child and the teacher.

## 2.1 Research Question

This study seeks to evaluate the impact of AI-powered social robots on the social communication skills of children with ASD. Specifically, it examines whether interactions with such robots can enhance engagement, empathy, and problem-solving abilities in educational settings. In this study, we investigate the following question: how does a chatgpt conversational robot agent improve the social skills of children with autism?

## 2.2 Hypothesis

* **H1:** Children interacting with AI-enhanced robots will exhibit increased conversational engagement compared to interactions with non-AI robots.

* **H2:** AI-powered robots that adapt to the communication styles of autistic children will facilitate more effective social interactions.

* **H3:** Problem-solving tasks conducted with AI-assisted robots will lead to improved reasoning skills in children with ASD.

## 2.3 Population

Our population will be composed of 50 children between 8 and 14 years old, with ASD verbal and with mild to moderate intellectual disability if present. Data collection will be in educational units of J.B. Thiéry Association, in collaboration with pedagogic and educational team. Consent forms will be provided to parents and all data will be anonymized.

## 2.4 Material

<figure><img src="image_3.png" /><figcaption>**Figure 1:** Nao Activities web interface, developed by Aldebaran</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Figure 2:** TeamChat interface ©Blue Frog Robotics</figcaption></figure>

Two social robots, NAO and Buddy, equipped with conversational modules powered by ChatGPT, will be utilized. The study will employ a mixed-methods approach, combining qualitative observations with quantitative assessments. Initial semi-structured interviews with educators will inform the development of interaction scenarios and set up the initial prompts. Subsequently, children will engage in structured tasks with the robots in classroom settings,

with interactions recorded and analyzed for indicators of engagement, communication, and problem-solving. For our experiments with Nao, we will use the NaoActivities interface developed by Aldebaran (Figure 1). This tool allows us to create a detailed prompt containing all the information needed for the activity. The information provided, such as the audience, the objectives of the activity, and step-by-step instructions, will help frame and contextualize the interaction. On Buddy's side, we will use the TeamChat application from Blue Frog Robotics with a robot-integrated interface in which we can enter a prompt (Figure 2). We will set up similar scenarios based on the detailed structure of Nao Activities in both cases so that we can compare our hypotheses on the two robots. The robots will then adapt to the child's responses by following the guidelines provided in advance. During the exchange, they can act as a humanoid robot, but we can also assign them a specific role to follow in the initial prompt to make the exchange more immersive. Buddy can also express emotions on its face depending on the content of the speech. In order to better frame the activity, constraints will also be imposed in the basic prompt. To maintain control over the information provided, we will include content restrictions and impose communication guidelines such as adopting a friendly and appropriate attitude towards the child.

## 3 Conclusion and Future Work

This preliminary study seeks to evaluate how AI-enhanced social robots can improve social communication skills, reasoning, and educational engagement in children with ASD. By integrating generative AI into social robotics, the project aims not only to facilitate more natural and adaptive interactions but also to address the mutual comprehension barriers described in the double empathy problem. Our initial phase combines qualitative input from educators and clinicians to design contextually relevant prompts and conversational scenarios. These serve as the foundation for experimental classroom activities, which will be rigorously observed and analyzed. Early findings are expected to inform both theoretical insights and practical approaches to human-robot interaction (HRI) tailored to neurodivergent populations. The main limitations we could encounter in this study would be logistical and technical, notably concerning the robot's voice recognition capabilities, as well as the question of data security.

Future work will proceed along several key directions:

* **Longitudinal Assessment:** We will implement longitudinal studies to evaluate the sustained impact of AI-powered robotic interventions on social communication, problem-solving, and educational outcomes.

* **Adaptive Algorithm Development:** The conversational modules will be enhanced with adaptive learning algorithms capable of dynamically adjusting to individual communication styles and developmental trajectories.

* **Generalization:** We plan to expand the study across multiple educational settings and diverse participant groups to assess the generalizability of the findings.

Through this multidisciplinary and iterative research agenda, we aim to contribute to the design of socially assistive technologies that support the autonomy, social inclusion, and educational success of children with ASD.
Acknowledgments

The authors wish to acknowledge the use of ChatGpt in writing this paper. We used the tool to assist with improving the language in the paper. The paper accurately represents the authors' underlying work and novel intellectual contributions.

We would like to thank the Association Jean-Baptiste Thiéry (Maxéville, France) for making its robots and testing grounds available to us. We have no conflict of interest.

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