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Abstract

Assistive Technology can be a highly effective tool in supporting students with Learning Disabilities (LD) in addressing foundational academic skill gaps as part of academic and behavioural one-to-one instruction. However, there are barriers to administrators wanting to equip in-service educators to integrate assistive technology into special education contexts, such as in-service educators’ technology acceptance and the need for effective in-service training. This article explores a model for supporting in-service educators to integrate assistive technology into an existing academic and behavioural one-to-one instruction program for students with LD through a partnership with a nonprofit educational provider and a university’s social robotics laboratory. We applied a co-design approach and followed a human-centred design methodology, incorporating a technology acceptance model to support educators in broadly integrating assistive technology into existing research-based programs for students with LD.

Keywords

Learning disabilities assistive technology academic and behavioural instruction technology acceptance user-centred design training professional development

The Learning Disabilities Society (LDS) is a non-profit educational provider that supports students with LD using assistive technologies. Through a visionary partnership with a university’s social robotics laboratory, the team integrated a social robot as an assistive technology to support in-service educators working with students with LD into an existing academic and behavioural one-to-one instruction program. Guided by the process of co-design and technology acceptance model, the integration of assistive technology (AT), a social robot becomes not just a tool but a catalyst to support educators at LDS.

Introduction

With the global movement towards inclusive education models in many developed nations, most students diagnosed with LD are learning in mainstream schooling environments (Yuen et al., 2005). While there are significant social, emotional, and learning benefits to inclusive education, students with LD are unlikely to receive the individualised instruction and support they need from a mainstream classroom placement alone, leaving them “included but underserved” (Crockett et al., 2012, pp. 405–436). As such, students with LD benefit from academic and behavioural one-to-one instruction - an evidence-based, high-leverage practice (Aceves & Kennedy, 2024) that provides targeted support in areas of academic challenge. This approach allows educators to help students develop strategies to overcome foundational skill gaps within a one-to-one or small-group context (Fuchs et al., 2008; Gerzel-Short & Hedin, 2022). To keep students with LD on-task and maximise their academic support, McLeskey (2017) identifies three key high-leverage practices¹ (HLPs): academic and behavioural instruction, integration of AT, and opportunities for active engagement. Note that HLPs are key practices that teachers should learn and be able to implement when teaching students.

Assistive technologies are often touted for their ability to capture students’ interest during learning experiences; however, the value and efficacy of AT for students with LD is highly dependent on the ability, skills, and commitment of the educator designing the learning experience and delivering instruction (Ertmer & Ottenbreit-Leftwich, 2010; Love et al., 2020). Further, studies have shown that within the population of students with disabilities, individuals with high-incidence disabilities, such as LD, reported far lower rates of technology use than peers with low-incidence disabilities (Bouck, 2016; United States Department of Education & Rehabilitative Services, 2018).

This finding suggests the importance of investigating barriers to engagement with AT while supporting students with LD, which is currently underutilised by educators within academic and behavioural instruction (Bouck, 2016; Boyle & Kennedy, 2019). For students with disabilities, using AT can offer opportunities beyond simply accessing curriculum or learning opportunities and support them to become more active participants in their learning and make meaningful progress toward learning goals (Okolo & Diedrich, 2014). Despite its potential advantages, Boyle and Kennedy (2019) and Bouck (2016) assert that technology has been underutilised for students with LD due to barriers regarding educators’ lack of experience when implementing technology in learning environments.

A national US study by Wei et al. (2011) found that despite more students with LD receiving instruction in general educational classrooms, most teachers in general education settings reported receiving little or no preparation for the best methods for teaching students with disabilities, such as using AT. Okolo and Diedrich (2014) identified additional barriers to integrating technology into the educational programming of students with LD, including lack of access to technology, lack of teacher time to learn and implement technology, and restrictions on loading or using software or applications. These barriers often serve to create resistance to the use of technology from educators working with students with LD, in addition to possible resistance from parents/families who may view technology as an unhelpful distraction or redirection from pencil/paper-based skills, such as printing and handwriting (Hirsch et al., 2019). Given these identified barriers and challenges to supporting educators in integrating technology into educational programs and learning outcomes for students with LD, integrating an AT that is new to all the instructors at the non-profit educational organisation was presumed to be a challenging endeavour. As such, the team began by using a co-design approach involving all stakeholders to jointly develop a new training program and protocol for implementing a new AT component into students’ existing academic and behavioural instruction program. In addition, we drew on the Technology Acceptance Model (TAM) (Davis et al., 1989; Ghazali et al., 2020; Venkatesh et al., 2003) to understand instructors’ experiences and perspectives about technology and technology use in academic and behavioural instruction for students with LD. This insight has allowed us to more appropriately plan the content, structure, sequence and ongoing support needed for the success of the new AT.

This paper shares practices from implementing a new AT approach by the administration of a nonprofit educational organisation delivering academic and behavioural instruction to students with LD. We draw on lessons learned from this work to provide a fulsome perspective of the challenges and successes related to the implementation, training, and retention of new AT use by educators for students with LD.

To integrate a social robot into LDS’s existing Assistive technology toolkit, the team leveraged QT, a small humanoid capable of speaking and expressing through head and hand gestures. Backed by evidence of its positive impact, especially on children with Autism Spectrum Disorder (ASD), the team find it a promising tool for instructors to foster engagement and enjoyment during one-on-one academic and behavioural instructional sessions with students facing LD.

Engaging Educators in Using New AT: Social Robots as a Case Study

AT is any device, software or equipment used to maintain or improve the functional capabilities of a person. Assistive technologies are frequently used in academic and behavioural instruction to support students with LD with their learning and demonstrate their learning and mastery of academic skills. They can support students with LD in three key areas: (1) providing opportunities for greater learner independence (e.g., a student with a written output challenge who requires a scribe may be able to type independently); (2) the ability to receive or engage with learning material in a way that is easier for a learner (e.g., a pen that scans text and reads it back to a student who struggles with reading); and (3) creating an engaging learning environment through the use of multi-modal learning that helps students stay on-task during instruction (Chiong & Shuler, 2010; Hirsch et al., 2019). These ATs can be powerful facilitators for learning, supporting students with LD in meeting learning objectives during academic and behavioural instruction, leading to improved academic outcomes.

Social robots are a relatively new AT that has been implemented in a range of special education contexts. Social robots are “designed to interact with people in human-centric terms and to operate in human environments alongside people” (Breazeal et al., 2016, pp. 1935–1972). They are designed to interact socially with the user through verbal and non-verbal expressions (e.g., speech, performing physical gestures, using facial expressions). These interactions, combined with the robot having some level of autonomy, make the robots appear social (Henschel et al., 2021). These robots have a range of physical appearances, from human-like (humanoids) to zoomorphic (animal-like), mechanoid (machine-like) or cartoon/toy-like. They can be operated using different modes of function, including fully autonomous, semi-autonomous, and WoZ (Wizard-of-Oz, invisibly controlled by an operator) (Bartneck et al., 2020; Scassellati et al., 2012) and perform tasks without human intervention. When fully autonomous social robots sense their environment, make decisions, and perform tasks without human intervention, semi-autonomous and wizard-of-oz controlled social robots are partially pre-programmed and partially or fully operated by humans. Note, in many successful AT applications, semi-autonomous social robots are being employed with human professionals (e.g., teacher, instructor, therapist), activating some autonomous behaviours or interaction games with the robot, i.e. via a tablet computer or other dedicated interfaces. This mode of robot operation allows the human professional to stay ‘in-the-loop’ and in control of the interaction with a child and the session as a whole. For example, a humanoid robot was used in this way for over a year by staff who worked with children in a nursery school for children with Autism Spectrum Disorder (Syrdal et al., 2020).

Given the evidence for using AT as an effective tool in engaging students with LD, we sought to investigate the impact of social robots on student engagement during their existing academic and behavioural instructional programs. The nonprofit educational organisation that trialled the social robot serves over 500 students yearly in their one-to-one instruction program. Instruction is delivered by approximately 35 highly experienced instructors, most of whom had minimal experience integrating AT into instruction.

The QT robot (Figure 1, left), developed by LuxAi (Luxembourg), was selected for trials due to its previous successful application with students with disabilities in clinical and educational settings. This small humanoid robot can gesture with its head and hands, exhibit facial expressions, and speak. While it appears autonomous to a child/learner, it is controlled by educators via a tablet, allowing them to trigger specific behaviours and interactive games (Figure 1, right). This mode of robot operation ensures that educators remain in control of the interaction and the overall session. The QT robot was originally designed to support children with ASD to deliver interventions for behavioural and emotional support (Pinto Costa et al., 2018, 2019).

Figure 1.

(Left) QT, an expressive small humanoid robot developed by LuxAI². (Right) Study setup. An instructor and a student interact with the QT robot during a session.

In addition to its initial applications for children with (ASD), the QT robot has been used to elicit empathy through storytelling in university students (Spitale et al., 2022) and support children with their handwriting skills (Tozadore et al., 2022). To further support students during academic and behavioural instruction, the organisation (LDS) and the university partner developed an intervention using the QT robot to help students stay on-task during the session and learn self-regulation techniques that hold the potential for generalising to other educational experiences (for further information about the intervention, see (Azizi et al., 2022; Azizi et al., 2023)). However, based on previous research and the organisation’s administration’s experience implementing new initiatives, careful thought and planning were needed to engage the organisation’s educators in the intervention and employ it within the students’ existing academic and behavioural instructional programs.

Designing a technology solution for LDS did require a deep understanding of the challenges faced by students with LD. The team also studied instructors’ teaching methods and needs. Additionally, exploring a social robot as a teaching tool involved assessing its instructional role, potential to motivate students, and activities to address these challenges. Investigating the above aspects was essential to ensure that the integration of AT met the needs of instructors and students. As this integration was in a real-world setting, the design process aimed to integrate AT without altering the instructional program’s goals or curriculum, accommodating diverse LD. Using a co-design approach and a human-centred methodology, the team developed and tested prototypes in real-world settings, leading to the creation and long-term evaluation of the AT.

Figure 2.

Five Phases of the Design Thinking Process. Adapted from (Brown & others, 2008). We employed a co-design approach in each of the phases.

Providing Context-Appropriate Solutions through a Co-Design Approach

We employed a co-design approach and followed the ‘Design Thinking’ method by Ideo (Brown & others, 2008) to actively and equitably engage stakeholders throughout the research and development process while keeping their needs and insights at the centre. The Design Thinking method is iterative and non-linear and consists of five stages: Empathize, Define, Ideate, Prototype, and Test (Figure 2). Our team is comprised of educators, administrators and human-robot interaction (HRI) researchers. From the project’s inception to its implementation, we held online weekly or bi-weekly co-design and feedback sessions to ensure alignment with project goals and technical feasibility, with the flexibility to revisit the previous stages of the design process as needed. Co-design activities included expert interviews, brainstorming sessions, interactive prototypes, feedback loops, prototype approvals and pilot testing.

1. Empathize. In this phase, the research team conducted online interviews and meetings with the stakeholders (educators and administrators) at LDS. These discussions aimed to gain a more profound and personal understanding of the concerns and needs related to students with LD and the instructors providing their one-to-one instruction sessions at the organisation. We gathered information related to instructors’ time constraints (e.g., for training on new technology) and conducted secondary research on the pedagogical techniques implemented for students with LD, as well as their individualised program-related tasks and learning sequences.

2. Define. In this phase, the team analysed the core problems, challenges and needs identified in the empathize phase. This included gathering information on how these issues were currently being addressed within the organisation. Narrowing down the most significant and impactful user needs was crucial to defining a clear problem statement. This process resulted in the identification of the following problem statements: How can we develop a social robot as a tool for instructors to provide one-to-one instruction for students with LD with the aim of increasing engagement to mitigate off-task behaviours and improve learning outcomes? How can we assess the developed system with the users? These statements generated key questions for the next phase, such as ideating on students’ most significant distraction behaviours, key learning goals, and the system’s ease of use.

3. Ideate. After thoroughly identifying potential challenges and barriers during the ideation phase, the team initiated a collaborative exploration to address these issues through the design of the intervention and the functionality of the robot and interface. This process highlighted several essential aspects for the intervention design: (1) ease of use; (2) using current session structure and processes (3) adaptation of current teaching strategies into the robot’s programming/interface (such as redirection strategies, targeted encouragement, mindfulness/breathing exercises, educational games), (4) clarifying the robot’s role and moderation style. Several redirection strategies were discussed to mitigate distractions, including jokes, riddles, games, breathing exercises, movement exercises, and positive self-talk. In addition, the team also brainstormed ideas to assess the intervention. Leveraging the team’s diverse expertise in education, technology, HRI, and robotics ensured the generation of ideas was user-centred, diverse, and not limited to a particular domain.

4. Prototype. During this phase, the goal was to design the intervention to avoid creating barriers to implementation, recognising that even small barriers could make the intervention seem ‘too difficult’ or undesirable to educators. Key aspects of design to consider for increasing the success of the AT intervention include:

a. Instructional Space Goal: To ensure that the AT intervention did not disrupt the teaching space or the flow of the student sessions, we first gained a thorough understanding of the instructional space setup and the daily movement of both instructors and students, as multiple individuals use these rooms each day. Typically, instructors and students sit facing each other with learning materials placed on a table between them (Figure 1, right). We positioned the robot on a separate table within easy reach of both the student and instructor (Table 1). This setup ensured that the furniture and AT (robot) placement did not interfere with other instructional processes or teaching materials in the room.

b. App interface Goal: To make the app as simple and user-friendly as possible. We began with a simplistic version of the app and improved it iteratively based on educator’s feedback. This feedback covered aspects such as the display of session workflow (warm-up, main activity, good-bye), grouping off-task behaviours, game choices, the robot’s features (e.g., dialogues, gestures, facial expressions) and app design elements (e.g., colors, size of elements, images). Several iterations were conducted to these features.

c. Intervention fit Goal: To assess the impact of AT in supporting educators with one-to-one academic and behavioural instruction for students with LD, it was essential for the intervention to align with the instructor’s current teaching practices and the student’s existing program goals. This ensures that the developed solutions effectively address users’ needs and were more likely to be accepted long-term. The app was designed with pre-defined robot actions and responses and was under the full control of the instructor. While instructors selected the segments based on their lesson sequence, the robot autonomously executed the chosen activities, using speech and gestures. Instructors used the app to prompt the robot, adjusting to students’ engagement levels, individual learning goals and behavioural support needs during sessions, similar to how they usually provide specific feedback or praise to students or redirect them as necessary. The robot’s behaviour, including the speed of speech and gestures, was pre-programmed based on prior instructor feedback.

5. Test. Before full implementation, we trialled our design of the instructional space, the app interface, and the intervention fit with a few volunteer instructors. During this trial phase, several challenges were identified:

Challenge 1 - Technological workload on Instructors: Providing separate tablets to instructors and students during the intervention aimed to support a fluid interaction between the users and the robot. However, our findings revealed that managing the two tablets while simultaneously tracking student progress posed challenges for instructors. For instance, during interactive games where the robot served as a motivator, coordinating turn-taking and assisting students disrupted the game’s interaction flow. Note that the commercially available QT robot included two tablets for similar therapeutic applications in ASD contexts. Our findings show the importance of carefully adapting commercially available systems to specific use cases and educational contexts, particularly for supporting the instruction needs of children with LD.

Solution: To address this challenge, we simplified the system by removing one tablet from the system. From that point onwards, both participants interacted with the robot using a single tablet. Typically, instructors manage the tablet, and when a student wishes to engage in a game, the instructor passes the tablet to the student.

Challenge 2 – Need for more diverse/engaging content: Initially, we developed two games for students, “Tic-Tac-Toe” and “Spot It”. While the students enjoyed the two games, they desired additional game experience with the robot. The game’s purpose was to distract the student from the challenging task and reduce feelings of stress and anxiety before re-engaging with the task.

Solution: Two additional collaborative games were developed: “Balloon Popit” and “Recall it”, each with added complexity to offer greater variety to students. Responding to feedback from the educators before full-scale implementation of the AT intervention tools was essential. Ensuring the AT was easy to use, varied, and engaging for students were key elements of the user-centred approach that informed our intervention development before dull implementation.

Since the Design Thinking approach is non-linear, we frequently revisited earlier phases to enhance our understanding. Following the pilot study, the team identified areas for improvement and iterated on the method accordingly.

Assessing the acceptance of AT among instructors was important to understanding various aspects, such as ease of use, perceived usefulness, enjoyment and intentions, which are critical in educational settings. Aware of the Technology acceptance model, a structured empirical framework, the team saw this as crucial in gauging users’ acceptance and adoption of AT.

Using a Technology Acceptance Model (TAM) to Evaluate Users’ Acceptance Towards AT

Research demonstrates that educators come to work with diverse knowledge, experience, and attitudes towards AT. Prior to introducing social robots to the instructors working at the nonprofit educational organization, the use of AT in sessions with students was low, so this, combined with findings from other research studies, led us to assume that instructors may be resistant to implementing new AT, or that ongoing support was needed. The goal of developing a fulsome understanding of instructor acceptance and prior use of AT was to identify what training and initial and ongoing support would be required to successfully implement a new AT in one-to-one academic and behavioural instruction programs for students with LD. The barriers educators face when using AT vary based on their background and general attitudes toward technology in general (Okolo & Diedrich, 2014). Without support, many educators may not see the value of AT or have the time to explore its usefulness. This is increasingly true with more complex or advanced AT, like social robots, as preconceived perceptions of the technology being “too complicated to use” would add additional challenges to a successful implementation. By understanding educators’ acceptance and prior experience with AT, specific support or professional development for educators can be developed to address these barriers and help them implement AT into their practice.

The TAM (Davis, 1985; Venkatesh, 2022; Venkatesh et al., 2003) is a widely used theoretical model in the field of information systems to predict and explain user acceptance of computer-based information technology products and services. The model has been extensively adopted and tested in various domains and has been found to be a valid and reliable predictor of user acceptance. It has been used as a framework for understanding and improving technology adoption and diffusion in organisations and for developing strategies for improving design and implementation. While other more complex technology acceptance models have been developed in research, for our in-situ studies, we needed to choose a reliable measure that is easy to administer, i.e. does not add significantly to the educators’ workload. Using the TAM, we identified extrinsic and intrinsic factors that would impact the implementation and longevity of the intervention by instructors and are as follows:

1. Perceived usefulness refers to the degree to which a person believes that using a particular technology will enhance their job performance or overall quality of life.

2. Perceived ease of use refers to the degree to which a person believes that using a particular technology will be simple and easy to use. The future use of technology is an essential factor to consider when introducing new technology.

3. Attitudes towards technology concern the value a person perceives for the technology.

4. Behavioural intentions indicate a person’s plan to use technology in the future.

5. Attitude Toward Usage refers to understanding the acceptance of using the technology.

Table 1 shows examples of users’ responses for each TAM model factor, capturing educators’ perspectives and experiences with a new technology.

Table 1.

Examples of Instructor’s and Students’ Responses (Azizi et al., 2022, 2023).

TAM Model Factors	Instructors’ (I) and Students’(S) Responses
Extrinsic Motivation (Perceived Usefulness)	I1. “The robot’s involvement and praise helped the student feel proud and positive and encouraged him (a student) to finish.”
Extrinsic Motivation (Perceived Usefulness)	I2. “Helpful for getting the students to stay on track/focus and help take pressure off me to do this.”
Intrinsic Motivation (Enjoyment)	I1. “QT is effective as a reward system and my student enjoyed the interactive portions. QT is a fun addition to the classroom.”
Intrinsic Motivation (Enjoyment)	S1. “The QT robot helped by Praising (“You did a good job today.”, “Great Effort”). “Breathing, movement break and jokes helped. The QT robot motivated me.”
Perceived Ease of Use	I1. Most instructors perceived the robot to be more useful and easier to use after the last interaction.
Perceived Ease of Use	I2. “QT can be a distraction at times for students, but I’ve found that the more they meet him the less distracting he is. I think QT is good for motivation.”
Behavioural Intentions to Use	I1. Most of the students wanted to use the QT robot in the future.
Behavioural Intentions to Use	S1. Most of the students wanted to use the robot either “always” or “often”.
Attitude Toward Usage	I1. “The strategies led by QT are helpful. Students respond better when QT leads them than when I do. Diversity in lessons, engagement, and motivation tools. It helps give them a goal to work towards and be involved in fun and engaging activities.”
Attitude Toward Usage	S1. “The robot’s strategies helped. I enjoyed hearing from the robot and wanted to explore more strategies. The QT robot’s jokes and movement breaks helped. Many students enjoyed having the robot and rated their experience as “fantastic” or “really good”.

Support Needs for Educators Implementing AT

Using the intervention trial and TAM questionnaire helped us understand what ongoing support instructors would need when using the intervention. These needs included opportunities for instructors to observe the intervention during one-to-one instructional sessions, the option of in-person support or a co-instructor when working with the robot, and a community of practice for connecting with peers who were also using the social robot into their sessions. These support structures were integral to the implementation, leading to a much stronger uptake and use of social robots during one-to-one sessions than other ATs available at the organisation. Additionally, LDS uses a variety of assistive software tools for online learning and resources for at-home study (e.g., tools for advanced digital writing and reading support or educational software to promote math, science, and literacy skills.)

The collaboration between the LDS and the social robotics lab not only resulted in integrating the AT, a social robot, into existing academic and behavioural instructional programs but also expanded different applications for students with or without learning difficulties. It supported the development of “in-house” expertise at LDS for programming the social robot to expand new applications and interventions for students to foster social and academic skills.

Building Skills for Ongoing AT Learning and Applications

Another positive outcome of the implemented support systems was the creation of a mechanism for ongoing communication between educators and the leadership team. This mechanism allowed for technical and professional support with the use of new and existing types of AT during academic and behavioural instruction instruction. This resulted in the creation of new programming and additional functions developed for the robot based on instructors’ use or identified student needs. The partnership between the educational organisation and the robotics lab supported the development of ‘in-house’ expertise for programming the social robot, allowing expansion to the suite of activities, games, commands, responses, and actions that the robot can do under the control of the user-friendly interface created during the collaborative design process. Examples of this are the creation of social and emotional programming for small group programs (such as summer camps and a parent-participation preschool program) that encourage self-regulation and peer-to-peer interactions and games that reinforce academic skills (such as phonics, identification of letter sounds, and CVC/sight words). When instructors used the QT robot for students with LD, they mentioned that QT’s encouragement and games helped students reach their session goals, kept them focused and relieved their pressure. The robot offered diverse breaks, motivating dialogues, engaging activities (e.g., jokes, riddles, games, physical exercises, breathing exercises, positive self-talk), and praised students during mediation. Instructors noted that QT’s strategies to refocus students were effective, and students responded better to QT’s breathing and movement exercises than to those led by the instructor (without the QT robot). The educators who identified new functions or games for the robots also became great ‘champions’ for their use with students. Similar to the findings of (Wong & Cohen, 2015), when investigating the use of assistive technology by teachers for students with visual impairments, having multiple champions of assistive technology, rather than a single interested educator, was essential for creating a culture where assistive technology is collectively embraced as it provides substantive opportunities for sharing practice, finding support, and navigating challenges. Details on creating a culture where assistive technology is collectively embraced as it provides substantive opportunities for sharing practice, finding support, and navigating challenges. Overall, to integrate a new assistive technology at LDS, we incorporated the following steps (Figure 3).

Figure 3.

Steps to integrate a new assistive technology (AT).

Implementing AT into educational programs at LDS enhanced individualised academic interventions for students with LD. Using the social robot improved students’ on-task behaviour, goal completion rates, and engagement during one-on-one sessions, making learning more enjoyable. However, challenges such as limited teacher training and resources hinder their integration into general and special education practices.

Conclusion

Implementing AT, such as social robots, into the educational and learning support programming for students with LD is a research-based strategy that supports learners engaging in individualised academic interventions. Our work shows that social robots can support students with LD in one-to-one instructional sessions when developed with human-centred design. They can potentially enhance students’ on-task behaviour and goal completion rates, reduce off-task behaviours and improve overall engagement for students with LD. Additionally, students with LD may find these robots enjoyable and supportive during learning activities. By equipping learners with tools and confidence, the new AT (social robot) enable them to better meet the demands of both current and future learning. However, integrating AT into the teaching practice of generalist and special education educators is an area of demonstrated challenge, with few easy solutions given the nature of schools/educational providers, teacher training, resourcing, and availability of support for this work. While exploring interventions using AT is important for developing evidence-based practice in this area, understanding the barriers that continue to make this work challenging for the educators tasked with this work is equally important. Despite how promising they may be to learners, interventions or uses of AT that are seen as unfeasible or too difficult for educators are unlikely to be successful.

This paper highlights the need to carefully consider how new research-based AT tools can be leveraged into effective educational interventions so that administrators, in-service educators and paraprofessionals understand their value, are willing to explore them in relation to their own practice and are able to effectively use the tool both during the trial phase and into the future. Our exploration of a model for administrators to engage in-service educators in integrating AT within their existing teaching practice highlights the benefits of engaging deeply with the educators responsible for implementing AT interventions. The use of carefully selected tools, such as the co-design approach, human-centred design methodology and TAM, provides a detailed understanding of the needs and abilities of the user/educator and how the tool needs to work in a particular context. This information makes the integration of AT more feasible and successful, which is essential in supporting students with LD in accessing the tools and learning they need to thrive throughout their lifespan. This work elucidates the benefits of collaborative approaches between the AT experts designing interventions/technologies and educational experts who may or may not take up the intervention/technology based on the chosen approach, design, feedback and ongoing support.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was undertaken, in part, thanks to funding from the Canada 150 Research Chairs program.

ORCID iD

Shruti Chandra

Notes

Author Biographies

Shruti Chandra is an Assistant Professor at the Department of Computer Science at the University of Northern British Columbia (UNBC) in British Columbia, Canada. She holds a Joint PhD degree in Electrical and Computer Engineering from École Polytechnique Fédérale de Lausanne, Switzerland, and Instituto Superior Técnico, Portugal. During the time of this research, she was a postdoctoral fellow at the Social and Intelligent Robotics Research Laboratory at the University of Waterloo. Her research focuses on developing social robots to enhance people’s well-being in education and healthcare, with a strong emphasis on practical and real-world applications.

Jennifer Fane holds an interdisciplinary PhD in education, public health, and social policy from Flinders University. She was the Director of Education at LDS during the time this research was conducted. She is currently the Lead Research Associate, Education and Skills, at the Conference Board of Canada, leading research in the area of neurodiversity.

Negin Azizi holds a Master’s degree in Electrical and Computer Engineering from the University of Waterloo. During this research, she served as the primary student researcher at the Social and Intelligent Robotics Research Laboratory at the University of Waterloo, specializing in assistive robotics for educational applications. She is currently a Software Engineer at Myant, where she continues to develop innovative technology solutions to enhance individuals' quality of life.

Mike Gray holds a Masters degree in Biomedical Engineering from the University of Toronto. During the time this research took place, Mike was the Assistive Technology Manager at LDS. He is currently employed by the Greater Victoria Public Library in Victoria, British Columbia.

Melissa Sager has had the privilege of working with educators and students in a variety of K-8 settings since 2007. She was the Associate Director of Learning Support at LDS during the time this research was conducted and is currently an Elementary Support Teacher in the Greater Vancouver area. Melissa holds an MA in Education and Human Development from the George Washington University.

Kerstin Dautenhahn, Dr. rer. nat., IEEE Fellow, Fellow of the Royal Society of Canada, is a Full Professor and Canada 150 Research Chair in Intelligent Robotics at the University of Waterloo in Canada. She is a member of the Department of Electrical and Computer Engineering. She directs the Social and Intelligent Robotics Research Laboratory. Her research areas are social robotics, human-robot interaction, assistive robotics, cognitive and developmental robotics. She has published 124 peer-reviewed journal articles (H-Index 94), and frequently gives invited keynote presentations at international conferences. She has several senior Editorial Roles in international journals.

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Integrating New Instructional Assistive Technology to Support Academic and Behavioural Instruction for Students with Learning Disabilities

Abstract

Keywords

Introduction

Engaging Educators in Using New AT: Social Robots as a Case Study

Providing Context-Appropriate Solutions through a Co-Design Approach

Using a Technology Acceptance Model (TAM) to Evaluate Users’ Acceptance Towards AT

Support Needs for Educators Implementing AT

Building Skills for Ongoing AT Learning and Applications

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iD

Notes

Author Biographies

References