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Artificial Intelligence: Solutions in Special Education
Abstract
The evolution of Artificial Intelligence (AI) has significantly transformed various fields, including education, by introducing advanced techniques and algorithms. This transformation encompasses tasks such as convergence, classification, identification, recognition, and beyond, integrating knowledge from sociology, psychology, ethics, computer science and pedagogy. The historical milestones of AI, from the early works of Turning and McCarthy to the recent advancements in Large Language Models (LLMs) like ChatGPT, demonstrate AI's progression and its impact on education. Recent advancements in AI, including Internet of Thing, Machine Learning, Augmented Reality, and Virtual Reality, have transformed the educational landscape, making it more inclusive and accessible for individuals with disabilities. This chapter proposes AI as solution to the field of special education particularly while addressing the needs of CwSN in mainstream settings. The research conducted in the past suggests that the use AI has greater advantages over traditional methods of teaching to the CwSN.
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The use of artificial intelligence in education (AIEd) has become increasingly significant globally. In China, there is a lack of research examining the behavioral intention toward AIEd among pre-service special education (SPED) teachers in terms of digital literacy and teacher self-efficacy. Building on the technology acceptance model, our study evaluated the aspects influencing pre-service special education teachers’ intention toward AI in education. Data was gathered from 274 pre-service SPED teachers studying at a Chinese public normal university of special education and analyzed using structural equation modeling (SEM). The results show that digital literacy is associated with the perceived usefulness and ease of use of AIEd, which influences SPED teachers' intention to use AIEd. Additionally, digital literacy significantly impacts the self-efficacy of SPED teachers. Given these results, AI designers in special education should comprehend the effectiveness and usability of AIEd for fostering behavioral intention formation. Simultaneously, special educational programs that identify key content and activities for digital literacy training should be developed, and educators should attempt to execute the relevant pre-service training to enhance the intention of pre-service SPED teachers toward AIEd.
With the advances of the internet and today's innovation, it has become conceivable to conduct teaching and learning activities remotely through the online platform. Existing research says that student’s attention state and learning result are strongly correlated. However, despite its importance, this can be a challenging task, as students in general taking an online class may be in a variety of different environments and may be multitasking or distracted by other factors. This review paper aims to address these challenges by exploring the opportunities offered by machine learning techniques in attention detection for effective online teaching and learning. By leveraging machine learning algorithms, which can analyze large volumes of data, including eye-tracking, facial expressions, and body movements, we can develop robust models for attention detection in online learning environments. This paper reviews the challenges specific to online learning, such as students' attention deficits and learning styles, and highlights the limitations of current attention detection methods. Furthermore, it provides recommendations to advance attention detection technology, emphasizing the potential of machine learning to enhance attention detection technology for effective online teaching and learning.
Introduction
Children with autism spectrum disorder may show delays in vocabulary development. Technology-based interventions could facilitate the teaching of different vocabulary skills; however, it is still not clear whether technology represents an added value.
Methods
The current review preregistered in PROSPERO evaluates the efficacy of technology-based interventions in vocabulary learning for children with autism spectrum disorder. We selected articles published in the period 2006–2022 from five databases.
Results
The results identified two group studies, one within subject design, nine single-case studies and one randomized controlled design in participants aged 0–16 years who had used technological devices to learn vocabulary. Overall, five of the 13 studies showed positive results of using technology-assisted intervention, six described mixed results, one described negative result, and one described no differences in technology-assisted intervention. The studies are divided into the categories of efficacy of technology and comparison between technology and non-technology.
Discussion
In summary, technology, such as tablets and computers, might be useful tools to improve vocabulary skills in certain children with ASD. However, the various degrees of impact found in the studies we reviewed indicate that personalized assessments, acknowledgment of previous experiences, and awareness of the context of usage are essential. The contrast with nontechnological approaches highlights the necessity for more detailed studies to pinpoint the precise conditions under which technology-based interventions can offer the most advantages.
Systematic review registration
[https://clinicaltrials.gov/], identifier [CRD42021238758].
This research article makes an attempt to investigate the potential of Artificial Intelligence (AI) in enhancing the learning experiences, as well as student outcomes. As a result, it has developed a study that will be able to understand how the different AI tools, including machine learning, data learning, virtual reality (VR) and augmented reality (AR), automation, and so forth can be used to develop learning experiences and outcomes. Subsequently, a case study involving a mathematics classroom was used to collect data and confirm whether indeed AI led to improved learning experiences and study outcomes. The study confirms that AI resulted in positive outcome with positive performance measure sin academic performance, motivation and engagement, learning progression, and so forth.
Rehabilitation is an important and necessary part of local and global healthcare services along with treatment and palliative care, prevention of disease, and promotion of good health. The rehabilitation process helps older and young adults even children to become as independent as possible in activities of daily life and enables participation in useful living activities, recreation, work, and education. The technology of Artificial Intelligence (AI) has evolved significantly in recent years. Many activities related to rehabilitation have been getting benefits from using AI techniques. The objective of this review study is to explore the advantages of AI for rehabilitation and how AI is impacting the rehabilitation process. This study aims at the most critical aspects of the rehabilitation process that could potentially take advantage of AI techniques including personalized rehabilitation apps, rehabilitation through assistance, rehabilitation for neurological disorders, rehabilitation for developmental disorders, virtual reality rehabilitation, rehabilitation of neurodegenerative diseases and Telerehabilitation of Cardiovascular. We presented a survey on the newest empirical studies available in the literature including the AI-based technology helpful in the Rehabilitation process. The novelty feature included but was not limited to an overview of the technological solutions useful in rehabilitation. Seven different categories were identified. Illustrative examples of practical applications were detailed. Implications of the findings for both research and practice were critically discussed. Most of the AI applications in these rehabilitation types are in their infancy and continue to grow while exploring new opportunities. Therefore, we investigate the role of AI technology in rehabilitation processes. In addition, we do statistical analysis of the selected studies to highlight the significance of this review work. In the end, we also present a discussion on some challenges, and future research directions.
Artificial intelligence (AI) has emerged as an excellent tool across multiple industries and holds great promise for the government, society, and economy. However, the absence of a distinct consensus regarding the definition and scope of artificial intelligence hinders its practical implementation in government settings. This article examines the various methodologies, emphases, and goals within artificial intelligence, emphasizing its ability to enhance human capabilities in critical situations. Considering the present advantages and enhanced productivity brought about by AI adoption in trailblazing government departments, this study explores the possible benefits and limitations of AI usage in the public sector. By looking at the cross-disciplinary difficulties of public AI applications, such as language hurdles and service delays, this study highlights the necessity for a thorough knowledge of the risks, impediments, and incentives of employing AI for government services. The study hopes to provide insight into AI research's ultimate aims, including object manipulation, natural language processing, and reasoning. This study emphasizes the potential for greater productivity, simplified procedures, and reduced obligations by analyzing the pros and cons of using AI in the public sector. Further, organizational theory is considered a tool for figuring out how to deal with challenges and maximize possibilities associated with AI deployment. The theory is used as the conceptual framework to understand the benefits, opportunities, and challenges involved in using AI when providing government services. The results of this research help us better understand how AI may revolutionize public service delivery by stimulating new ideas and improving efficiency. This study covers critical questions about organizational theory's role in improving government AI adoption, the challenges governments have in adopting AI, and the potential benefits AI might offer public service delivery. The research recommends a strategic approach to AI adoption in the public sector, considering organizational, ethical, and societal implications while recognizing the possibility of AI's transformative impacts on governments' service provision.
This article explores the transformative role of Artificial Intelligence (AI) in supporting and empowering individuals with disabilities. It delves into various AI-driven innovations and technologies that are breaking down barriers across digital accessibility, communication, personalized learning, and health monitoring. The discussion highlights the significant strides made in enhancing web and digital platform accessibility for the visually impaired through AI-powered voice assistants and screen readers, as well as mobility solutions for those with physical disabilities. It further examines AI's impact on improving communication for people with speech and hearing impairments via real-time transcription services and sign language interpretation apps. Additionally, the article touches upon the customization of learning experiences for individuals with cognitive and learning disabilities through adaptive learning platforms, and the advancement of health monitoring and assistance through wearable technology and AI-driven prosthetics. While acknowledging the challenges, including ethical concerns and accessibility of these technologies, the article maintains an optimistic outlook on the potential for future advancements. The narrative underscores the critical importance of continued innovation and collaboration in leveraging AI to not only enhance the lives of people with disabilities but also to foster a more inclusive society.
Introduction
Digital accessibility involves designing digital systems and services to enable access for individuals, including those with disabilities, including visual, auditory, motor, or cognitive impairments. Artificial intelligence (AI) has the potential to enhance accessibility for people with disabilities and improve their overall quality of life.
Methods
This systematic review, covering academic articles from 2018 to 2023, focuses on AI applications for digital accessibility. Initially, 3,706 articles were screened from five scholarly databases—ACM Digital Library, IEEE Xplore, ScienceDirect, Scopus, and Springer.
Results
The analysis narrowed down to 43 articles, presenting a classification framework based on applications, challenges, AI methodologies, and accessibility standards.
Discussion
This research emphasizes the predominant focus on AI-driven digital accessibility for visual impairments, revealing a critical gap in addressing speech and hearing impairments, autism spectrum disorder, neurological disorders, and motor impairments. This highlights the need for a more balanced research distribution to ensure equitable support for all communities with disabilities. The study also pointed out a lack of adherence to accessibility standards in existing systems, stressing the urgency for a fundamental shift in designing solutions for people with disabilities. Overall, this research underscores the vital role of accessible AI in preventing exclusion and discrimination, urging a comprehensive approach to digital accessibility to cater to diverse disability needs.
This chapter examines the role of generative AI (GenAI) and robotics in special education, particularly for autism spectrum disorder (ASD). It highlights the revolutionary impact of GenAI and AI-powered educational technologies (AIEd) in early diagnosis, personalized learning, and fostering independence in individuals with ASD. Additionally, robotics is presented as a vital tool for enhancing social and communication skills. The integration of these technologies in education requires addressing ethical, privacy, and security concerns. The chapter emphasizes technological literacy, including prompt engineering, to optimize the use of GenAI and robotics. It also explores the unique relationship between individuals with ASD and robots, suggesting a promising future for these technologies in special education. The conclusion calls for ongoing research and development to create more equitable and accessible educational systems, harnessing the power of GenAI and robotics for individuals with ASD.
It is becoming increasingly clear that not all students require the same education, and the requirement of personalized education is increasingly in demand. The incorporation of adaptive learning (AL) has increased in recent years. However, research on this subject is still evolving at the university level. In this study, we investigated the impact of integrating an AL tool (CogBooks®) in a university course (statistics for decision making) taught in an innovative online modality called FIT (flexible, interactive, and with technology), in which the course is designed in the CANVAS® platform and uses Zoom® as a means of communication with students. Learning outcomes were compared between the FIT courses with or without AL and between AL strategies in online and face-to-face courses. It was clear that AL improved the students’ achievement regardless of the modality. In addition, we conclude that students achieve better in AL courses in the classroom than in distance courses. Satisfaction surveys favor a preference for FIT courses with AL over classroom classes with AL. Our results suggest that AL is a solid strategy for teaching undergraduate courses.
Graphical abstract
The significant advancements in applying artificial intelligence (AI) to healthcare decision-making, medical diagnosis, and other domains have simultaneously raised concerns about the fairness and bias of AI systems. This is particularly critical in areas like healthcare, employment, criminal justice, credit scoring, and increasingly, in generative AI models (GenAI) that produce synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including generative biases that affect the representation of individuals in synthetic data. This survey study offers a succinct, comprehensive overview of fairness and bias in AI, addressing their sources, impacts, and mitigation strategies. We review sources of bias, such as data, algorithm, and human decision biases—highlighting the emergent issue of generative AI bias, where models may reproduce and amplify societal stereotypes. We assess the societal impact of biased AI systems, focusing on perpetuating inequalities and reinforcing harmful stereotypes, especially as generative AI becomes more prevalent in creating content that influences public perception. We explore various proposed mitigation strategies, discuss the ethical considerations of their implementation, and emphasize the need for interdisciplinary collaboration to ensure effectiveness. Through a systematic literature review spanning multiple academic disciplines, we present definitions of AI bias and its different types, including a detailed look at generative AI bias. We discuss the negative impacts of AI bias on individuals and society and provide an overview of current approaches to mitigate AI bias, including data pre-processing, model selection, and post-processing. We emphasize the unique challenges presented by generative AI models and the importance of strategies specifically tailored to address these. Addressing bias in AI requires a holistic approach involving diverse and representative datasets, enhanced transparency and accountability in AI systems, and the exploration of alternative AI paradigms that prioritize fairness and ethical considerations. This survey contributes to the ongoing discussion on developing fair and unbiased AI systems by providing an overview of the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the emerging field of generative AI.
In inclusive education, students with different needs learn in the same context. With the advancement of artificial intelligence (AI) technologies, it is expected that they will contribute further to an inclusive learning environment that meets the individual needs of diverse learners. However, in Japan, we did not find any studies exploring current needs in an actual special needs context. In this study, we used the learning and evidence analysis framework (LEAF) as a learning analytics-enhanced learning environment and employed Active Reading as an example learning task to investigate the challenges and possibilities of applying AI to inclusive education in the future. Two students who attended a resource room formed the context. We investigated learning logs in the LEAF system while each student executed a given learning task. We detected specific learning behaviors from the logs and explored the challenges and future potential of learning with AI technology, considering human involvement in orchestrating inclusive educational practices.
AS D may be caused by a combination of geneticand environmental factors, including gene mutations andexposure to toxins. People with AS D may also have troubleforming social relationships, have difficulty with communicationand language, and struggle with sensory sensitivity. Thesedifficulties can range from mild to severe and can affect aperson's ability to interact with the world around them. Autismspectrum disorder (AS D) is a developmental disorder that affectspeople in different ways. But early detection of AS D in a child isa good option for parents to start corrective therapies andtreatment. They can take action to reduce the AS D symptoms intheir child. The proposed work is the detection of AS D in a childusing a parent’s dialog. The most popular Bert model and recentChatGPT have been utilized to analyze the sentiment of eachstatement from parents for the detection of symptoms of AS D.The Bert model has been developed by the transformers whichare the most popular in the natural language processing fieldwhereas the ChatGPT model is a large language model (LLM). Itis based on Reinforcement learning from human feedback(RLHF) that can able to generate the sentiment of the sentence,computer language codes, text paragraphs, etc. The sentimentanalysis has been done on parents’ dialog using the Bert modeland ChatGPT model. The data has been prepared from variousAutism groups on social sites and other resources on the internet.The data has been cleaned and prepared to train the Bert modeland ChatGPT model. The Bert model is able to detect thesentiment of each sentence from parents. Any positive sentimentdetection means parents should be aware of their children. Theproposed model has given 83 percent accuracy according to th
(PDF) Detection of Autism Spectrum Disorder (ASD) from Natural Language Text using BERT and ChatGPT Models. Available from: https://www.researchgate.net/publication/375198563_Detection_of_Autism_Spectrum_Disorder_ASD_from_Natural_Language_Text_using_BERT_and_ChatGPT_Models [accessed Dec 20 2023].
AI chatbots shook the world not long ago with their potential to revolutionize education systems in a myriad of ways. AI chatbots can provide immediate support by answering questions, offering explanations, and providing additional resources. Chatbots can also act as virtual teaching assistants, supporting educators through various means. In this paper, we try to understand the full benefits of AI chatbots in education, their opportunities, challenges, potential limitations, concerns, and prospects of using AI chatbots in educational settings. We conducted an extensive search across various academic databases, and after applying specific predefined criteria, we selected a final set of 67 relevant studies for review. The research findings emphasize the numerous benefits of integrating AI chatbots in education, as seen from both students' and educators' perspectives. We found that students primarily gain from AI-powered chatbots in three key areas: homework and study assistance, a personalized learning experience, and the development of various skills. For educators, the main advantages are the time-saving assistance and improved pedagogy. However, our research also emphasizes significant challenges and critical factors that educators need to handle diligently. These include concerns related to AI applications such as reliability, accuracy, and ethical considerations.
Occupational therapy (OT) and special education and rehabilitation are crucial fields dedicated to supporting individuals with disabilities and special needs, aiming to improve their functional abilities and overall quality of life. Recent advancements in artificial intelligence (AI) offer promising opportunities to revolutionize therapeutic practices in these fields. This scientific review paper explores the application of AI in occupational therapy and special education and rehabilitation, highlighting key applications, challenges, and ethical considerations.
The methodology involved a systematic search of electronic databases, including PubMed, Scopus, and Web of Science, using relevant search terms such as "artificial intelligence," "occupational therapy," "special education," and "rehabilitation." The search was limited to English-language articles published between 2013 and 2023. The selected articles provided valuable insights into the application of AI in occupational therapy and special education and rehabilitation. The findings were synthesized and analyzed to identify common themes, trends, and gaps in the literature.
The analysis revealed the potential of AI in improving assessment accuracy, personalizing interventions, enhancing independence, and improving social interaction and communication. However, challenges related to data privacy and security, bias and fairness, transparency and explainability, human-AI collaboration, and ethical use of AI-generated content must be addressed. By embracing AI technologies while addressing these challenges, healthcare professionals can optimize care and empower individuals with disabilities and special needs to achieve their full potential.
Future research and collaboration are necessary to fully harness the potential of AI in occupational therapy and special education and rehabilitation while ensuring its ethical and responsible integration.
Artificial intelligence has the ability to radically change how teachers interact with pupils who have special needs. Although the effects of AI and other technologies have been far-reaching in the private and public sectors, they have also had a major impact on the academic world, making learning easier for students with sensory, motor, cognitive, and other forms of impairments. The application of AI in the classroom has encouraged the development of more accessible and inclusive teaching practices for all pupils. This chapter analyzed how AI has influenced special education in the classroom. The study analyzed (a) AI's impact on SEN education and (b) AI's impact on SEN education through its positive effects on SEN educators. This chapter explores how AI has the potential to enhance the lives, careers, and working environments of educators. It also explores how special education supported by AI could personalize lessons for each student and encourage greater involvement on their behalf.
Sustainable education is a crucial aspect of creating a sustainable future, yet it faces several key challenges, including inadequate infrastructure, limited resources, and a lack of awareness and engagement. Artificial intelligence (AI) has the potential to address these challenges and enhance sustainable education by improving access to quality education, creating personalized learning experiences, and supporting data-driven decision-making. One outcome of using AI and Information Technology (IT) systems in sustainable education is the ability to provide students with personalized learning experiences that cater to their unique learning styles and preferences. Additionally, AI systems can provide teachers with data-driven insights into student performance, emotions, and engagement levels, enabling them to tailor their teaching methods and approaches or provide assistance or intervention accordingly. However, the use of AI and IT systems in sustainable education also presents challenges, including issues related to privacy and data security, as well as potential biases in algorithms and machine learning models. Moreover, the deployment of these systems requires significant investments in technology and infrastructure, which can be a challenge for educators. In this review paper, we will provide different perspectives from educators and information technology solution architects to connect education and AI technology. The discussion areas include sustainable education concepts and challenges, technology coverage and outcomes, as well as future research directions. By addressing these challenges and pursuing further research, we can unlock the full potential of these technologies and support a more equitable and sustainable education system.
This paper aims to investigate the possibility of combining humanoid robots, particularly the NAO robot, with a mobile application to enhance the educational experiences of children with autism spectrum disorder (ASD). The NAO robot, interfaced with a mobile app, serves as a socially assistive robotic (SAR) tool in the classroom. The study involved two groups of children aged three to six years old, exhibiting mild to moderate ASD symptoms. While the experimental group interacted with the NAO robot, the control group followed the standard curriculum. Initial findings showed that students in the experimental group exhibited higher levels of engagement and eye contact. However, certain limitations were identified, including the NAO robot’s limited capacity for concurrent interactions, language difficulties, battery life, and internet access. Despite these limitations, the study highlights the potential of robots and AI in addressing the particular educational requirements of children with ASD. Future research should focus on overcoming these obstacles to maximize the advantages of this technology in ASD education.
Brain–computer interface (BCI), an emerging technology that facilitates communication between brain and computer, has attracted a great deal of research in recent years. Researchers provide experimental results demonstrating that BCI can restore the capabilities of physically challenged people, hence improving the quality of their lives. BCI has revolutionized and positively impacted several industries, including entertainment and gaming, automation and control, education, neuromarketing, and neuroergonomics. Notwithstanding its broad range of applications, the global trend of BCI remains lightly discussed in the literature. Understanding the trend may inform researchers and practitioners on the direction of the field, and on where they should invest their efforts more. Noting this significance, we have analyzed 25,336 metadata of BCI publications from Scopus to determine advancement of the field. The analysis shows an exponential growth of BCI publications in China from 2019 onwards, exceeding those from the United States that started to decline during the same period. Implications and reasons for this trend are discussed. Furthermore, we have extensively discussed challenges and threats limiting exploitation of BCI capabilities. A typical BCI architecture is hypothesized to address two prominent BCI threats, privacy and security, as an attempt to make the technology commercially viable to the society.
Autism Spectrum Disorder (ASD) is normally detected and diagnosed by 18 months of age, the process is lengthy and often delayed as it is not considered to be urgent. Artificial Intelligence (AI) can facilitate early diagnosis of autism which would then vastly increase the chances of reducing social impairment in patients by enabling timely intervention. AI can also play a pivotal role in intervention via robots and virtual reality (VR), which can provide a safe means of practicing their social skills before applying them to humans. Additionally, AI provides the hope of making revelations at a genomic level that could aid in the prediction of the development of autism in the antepartum period, or maybe even as a potential pre-conception test.
Uncovering patterns and trends in vast, ever-increasing quantities of data has been enabled by different machine learning methods and techniques used in Artificial Intelligence (AI) systems. Permeating many aspects of our lives and influencing our choices, development in this field continues to advance and increasingly impacts us as individuals and our society. The risks and unintended effects such as bias from input data or algorithm design have recently stirred discourse about how to inform and teach AI in K-12 education. As AI is a new topic not only for pupils in K-12 but also for teachers, new skill sets are required that enable critical engagement with AI. AI literacy is trying to close the gap between research and practical knowledge transfer of AI-related skills. Teachers' AI-related technological, pedagogical and content knowledge (TPACK) are important factors for AI literacy. However, as teachers' perspectives, beliefs and views impact both the interpretation and operationalisation of curriculum. this study explores teachers' and teacher educators' understanding and preconceptions of AI to inform teacher education and professional development. To gain a comprehensive understanding of teachers’ conceptualisations regarding AI an anonymous questionnaire together with focus group discussions were employed. The qualitative content analysis underpinned by the theoretical framework Intelligent TPACK reveals that teachers' AI-related content knowledge is generally gained through incidental learning and often results in pre- and misconceptions of AI. Our analysis also revealed several potential challenges for teachers in achieving core constructs of Intelligent TPACK, examples of such challenges are vague and unclear guidelines in both policy and curriculum, a lack of understanding of AI and its limitations, as well as emotional responses related to participants' preconceptions. These insights are important to consider in designing teacher education and professional development related to AI literacy.
Since its inception, the Actiotope Model of Giftedness (AMG) has provided researchers with a useful model to explain the development of exceptionality. Rather than a focus on the individual, the model postulates that exceptionality is the outcome of a system that includes complex interactions between an individual’s current level of talent and their internal and external environment. To date, however, the statistical techniques that have been used to investigate the model, including linear regression and structural equation modeling, are unable to fully operationalize the systemic nature of these interactions. In order to fully realize the predictive potential and application of the AMG, we outline the use of artificial neural networks (ANNs) to model the complex interactions and suggest that such networks can provide additional insights into the development of exceptionality. In addition to supporting continued research into the AMG, the use of ANNs has the potential to provide educators with evidence-based strategies to support student learning at both an individual and whole-school level.
The use of artificial intelligence (AI) is becoming more prevalent across industries such as healthcare, finance, and transportation. Artificial intelligence is based on the analysis of large datasets and requires a continuous supply of high-quality data. However, using data for AI is not without challenges. This paper comprehensively reviews and critically examines the challenges of using data for AI, including data quality, data volume, privacy and security, bias and fairness, interpretability and explainability, ethical concerns, and technical expertise and skills. This paper examines these challenges in detail and offers recommendations on how companies and organizations can address them. By understanding and addressing these challenges, organizations can harness the power of AI to make smarter decisions and gain competitive advantage in the digital age. It is expected, since this review article provides and discusses various strategies for data challenges for AI over the last decade, that it will be very helpful to the scientific research community to create new and novel ideas to rethink our approaches to data strategies for AI.
Despite the influx of research examining various aspects of mobile-assisted language learning (MALL) applications (apps) over the past two decades, there have been no head-to-head studies that have investigated the comparative effectiveness of different mobile apps. The current study addresses this gap by directly comparing two of the most well-researched and popular MALL apps: Babbel and Duolingo. In this mixed methods study, adult learners (N = 59) engaged in studying Turkish as a foreign language using either Babbel (n = 27) or Duolingo (n = 32) for eight weeks. Participants then completed two exit assessments, including (1) a posttest gauging their development of various language skills (i.e., reading, writing, speaking, listening, vocabulary, and grammatical competence), and (2) a survey assessing their user experience (e.g., enjoyment, motivation, beliefs about effectiveness). The results of this study showed that although both the Babbel and Duolingo groups made progress, there were no statistically significant differences between their L2 learning gains. However, for the Babbel group, there was a stronger correlation between participants’ study time and their posttest scores. Finally, Babbel users also felt the app was more effective for learning grammar, speaking/pronunciation skills, and for learning about the target language culture. This study discusses the implications of these findings for researchers and MALL users more broadly.
Minecraft Education is a digital game-based learning platform that is thought to support the development of twenty-first century competencies and skills. The purpose of this study is to explore primary students’ experiences of using Minecraft Education during an innovative national project-based initiative. The initiative had two phases: 1) educational episodes for teachers and students on how to use the platform and 2) a national competition that required students to re-imagine a sustainable version of their community. We used a mixed-methods design with a sample of classes taking part in the initiative. First, third to sixth class students ( N = 173) completed a survey that examined: 1) learning opportunities with Minecraft Education, 2) ease of use, 3) usefulness, and 4) enjoyment. Eight focus group interviews were then conducted with a subsample of sixth class students ( n = 30). Students indicated that 1) there were good opportunities for learning with Minecraft Education, particularly for creativity and collaboration, 2) the platform was easy to use and useful and, 3) using Minecraft Education was enjoyable. Thematic analysis of the qualitative data identified five themes: ‘collaboration’, ‘opportunities for creativity’, ‘immersive learning environment’, ‘student engagement’, and ‘technology and digital skills’. This research highlights the value of innovative project-based learning activities with Minecraft Education for supporting student learning.
This study aimed to examine the impacts of computerized dynamic assessment (C-DA) and rater-mediated assessment on the test anxiety, writing performance, and oral proficiency of Iranian EFL learners. Based on Preliminary English Test (PET) results, a sample of 64 intermediate participants was chosen from 93 students. Running a convenience sampling technique, target test-takers were randomly divided into the experimental groups (C-DA) and control (rater mediated assessment). Following that, both groups had pretests for oral and written skills. The Science Anxiety Scale (SAS) was also used to gauge their level of anxiety prior to treatment. The experimental group’s participants then received C-DA. Rater-mediated assessment, on the other hand, was given to the control group. Both groups took the post-test for writing performance, oral proficiency, and test anxiety at the conclusion of the treatment. According to the one-way ANCOVA analysis, the post-test results for the two groups were different. Based on the results, the experimental group outdid the control group on the oral proficiency, writing performance, and test anxiety post-tests. Iranian EFL learners were able to improve both their written and oral skills while experiencing less test anxiety thanks to C-DA. Finally, the conclusions, the implications, the limitations, and the suggestions for further studies were provided.
The pandemic has catalyzed a significant shift to online/blended teaching and learning where teachers apply emerging technologies to enhance their students’ learning outcomes. Artificial intelligence (AI) technology has gained its popularity in online learning environments during the pandemic to assist students’ learning. However, many of these AI tools are new to teachers. They may not have rich technical knowledge to use AI educational applications to facilitate their teaching, not to mention developing students’ AI digital capabilities. As such, there is a growing need for teachers to equip themselves with adequate digital competencies so as to use and teach AI in their teaching environments. There are few existing frameworks informing teachers of necessary AI competencies. This study first explores the opportunities and challenges of employing AI systems and how they can enhance teaching, learning and assessment. Then, aligning with generic digital competency frameworks, the DigCompEdu framework and P21’s framework for twenty-first century learning were adapted and revised to accommodate AI technologies. Recommendations are proposed to support educators and researchers to promote AI education in their classrooms and academia.
Intelligent textbooks are a new form of digital textbooks that provides students with intelligent learning services, such as automatic question answering, adaptive navigation support, automatic linking, and personalized recommendation. Educators are investing much effort into the conversion of their paper-based textbooks and digital textbooks into intelligent textbooks. This chapter provides a brief introduction to the development of intelligent textbooks and reviews major adaptive technologies and the usage of intelligent textbooks in the past decade. We first summarize the development of intelligent textbooks and the existing authoring platforms. Then, the key intelligent technologies used in the intelligent textbooks are reviewed, including domain modeling, student modeling, and instruction technologies. Moreover, the empirical studies conducted to evaluate intelligent textbooks are summarized. This chapter would promote understanding of the current state of the technology and platform development of intelligent textbooks.
The interaction capabilities of Intelligent Social Agents, made possible by advances in artificial intelligence, are eliciting deep emotional bonding with users, leading researchers to reexamine the impact and potential uses of these human-machine relationships in education. In this work, we examine how one best-in-class ISA, Replika, might be affecting users socially, emotionally, and cognitively. Our results indicate that many use Replika as a social and cognitive mirror, facilitating their learning and development this way. The confluence of new functionality, product narrative, and user life stressors make ISAs an emerging tool for not only emotional support, but also cognitive support and development.
Common classroom teaching analysis, which has focused on counting and coding the teacher-student behaviors and discourse interactions, faces many difficulties as content-free, low efficiency, and small scale in analysis. To overcome these shortcomings, and foster high-quality classroom teaching, the current study proposes a blended human and Artificial Intelligence (AI) technology for classroom teaching analysis named as TESTII. It consists of five steps identifying teaching events, sequencing the pedagogies of classroom teaching structure, analyzing teacher-student interaction, interpreting teaching meaning, and providing improvement strategies for high-quality classroom teaching.
Student problem behavior refers to the students’ undesirable conducts and actions in schools. These behaviors severely affect students’ growth and development. In many cases, student problem behaviors are caused by the unsatisfied psychological needs, which are defined as the need deficiency. Hence, diagnosing students’ need deficiencies and regulating their problem behaviors are important educational tasks. The previous psychological studies have analyzed how distinct factors might affect student’s problem behaviors. However, the school teachers without such expertise may still encounter difficulties in applying these findings. Hence, this chapter describes the process of designing and implementing an intelligent teacher assistant, which could advise teachers and help them to handle the student problem behavior. Technically, it utilizes a task-oriented dialogue system to help identify the underlying reasons (i.e., the student need deficiency) behind their problem behaviors, and accordingly relies on a community question answering system to provide the advice. It also employs the semantic search technology to find the similar cases that have been well resolved by the experienced teachers.
Recent developments in Artificial Intelligence (AI) have generated great expectations for the future impact of AI in education and learning (AIED). Often these expectations have been based on misunderstanding current technical possibilities, lack of knowledge about state-of-the-art AI in education, and exceedingly narrow views on the functions of education in society. In this article, we provide a review of existing AI systems in education and their pedagogic and educational assumptions. We develop a typology of AIED systems and describe different ways of using AI in education and learning, show how these are grounded in different interpretations of what AI and education is or could be, and discuss some potential roadblocks on the AIED highway.
In this study we provide an in-depth review and analysis of the impact of artificial intelligence (AI) components and solutions that support the development of cutting-edge assistive technologies for children with special needs. Various disabilities are addressed and the most recent assistive technologies that enhance communication and education of disabled children, as well as the AI technologies that have enabled their development, are presented. The paper summarizes with an AI perspective on future assistive technologies and ethical concerns arising from the use of such cutting-edge communication and learning technologies for children with disabilities.
Science subjects at pre-tertiary and tertiary education levels are important for socio-economic and industrial development of any country; however, they are difficult for students to construct their concepts. In Tanzania, insufficient or lack of practical experiments are major challenges for science subjects due to insufficient or lack of laboratories, apparatus, expertise or reagents. Thus, this research assesses the effectiveness of paper-based against virtual laboratory experiences towards improvement of real (hands-on) chemistry practical in Tanzanian secondary schools focusing in Dodoma region. Chemistry virtual laboratory was developed and being deployed at Dodoma Secondary School for students who were never done practical sessions before to avoid biasness towards the study. The students were divided into three groups, namely paper-based and real laboratory as control groups (CG) and virtual laboratory as the experiment group (EG). Each group was further divided into two groups for the rest approaches forming six (6) groups. For EG, students were taught based on instructional approach which was enriched by computer animations in the computer laboratory. Results indicate that students who firstly attended virtual laboratory performed better in real laboratory than those who firstly attended real laboratory. Furthermore, the best progressive learning and performance for real experiments appears when the virtual laboratory preceded paper-based practical experiments. Thus, virtual laboratory is a very useful tool for learning chemistry practical not only to schools without laboratories but also to those with laboratories; and it should be considered by all the pre-tertiary schools in Tanzania and other schools in similar situations.
Many people adopting Smart Assistant Devices such as Google Home. Now a days of solely engaging with a service through a keyboard are over. The new modes of user interaction are aided in part by this research will investigate how advancements in Artificial Intelligence and Machine Learning technology are being used to improve many services. In particular, it will look at the development of google assistants as a channel for information distribution. This project is aimed to implement an android-based chatbot to assist with Organization basic processes, using google tools such as Dialogflow that uses Natural language processing NLP, Actions on Google and Google Cloud Platform that expose artificial intelligence and Machine Learning methods such as natural language understanding. Allowing users to interact with the google assistant using natural language as input and to train the chatbot i.e. google assistant using Dialogflow Machine learning tool and some appropriate methods so it will be able to generate a dynamic response. The chatbot will allow users to view all their personal academic information, schedule meetings with higher officials, automating the organization process and organization resources information all from within the chatbot i.e. Google Assistant. This project uses the OAuth authentication for security purpose. The Dialogflow helps to understand the users query by using machine learning algorithms. By using this google assistant we are going to use the Cloud Vision API for advancement. We will use Dialogflow as key part to develop Google assistant.
In recent years, artificial intelligence (AI) and learning analytics (LA) have been introduced into the field of education, where their use has great potential to enhance the teaching and learning processes. Researchers have focused on applying these technologies to teacher education, as they see the value of technology for educating. Therefore, a systematic review of the literature on AI and LA in teacher education is necessary to understand their impact in the field. Our methodology follows the PRISMA guidelines, and 30 studies related to teacher education were identified. This review analyzes and discusses the several ways in which AI and LA are being integrated in teacher education based on the studies’ goals, participants, data sources, and the tools used to enhance teaching and learning activities. The findings indicate that (a) there is a focus on studying the behaviors, perceptions, and digital competence of pre- and in-service teachers regarding the use of AI and LA in their teaching practices; (b) the main data sources are behavioral data, discourse data, and statistical data; (c) machine learning algorithms are employed in most of the studies; and (d) the ethical clearance is mentioned by few studies. The implications will be valuable for teachers and educational authorities, informing their decisions regarding the effective use of AI and LA technologies to support teacher education.
Nowadays individuals can readily set reminders to offload intentions onto external resources, such as smartphone alerts, rather than using internal memory. Individuals tend to be biased, setting more reminders than would be optimal. We address the question whether the reminder bias depends on offloading scenarios being framed as either gains or losses, both between-participants (Experiment 1) and within-participants (Experiment 2). In both experiments, framing of reminders in terms of gains resulted in participants employing a risk-averse strategy and using more reminders than would be optimal. Importantly, however, participants used reminders more optimally and were more willing to choose the risk-seeking option of remembering internally when reminders implied a loss. Based on metacognitive measures in Experiment 2, the reminder bias increased the more underconfident participants were about their memory abilities in both framing scenarios. Framing did not alter this relationship between erroneous metacognitive underconfidence and reminder bias but provides an additional influence. We conclude that emphasizing the losses (costs) associated with external reminders helps in achieving more optimal decisions in offloading situations, and that in addition to cognitive effort and metacognitive judgments, framing needs to be considered in improving individuals’ offloading behavior.
Vehicular technology has recently gained increasing popularity, and autonomous driving is a hot topic. To achieve safe and reliable intelligent transportation systems, accurate positioning technologies need to be built to factor in the different types of uncertainties such as pedestrian behavior, random objects, and types of roads and their settings. In this work, we look into the other domains and technologies required to build an autonomous vehicle and conduct a relevant literature analysis. In this work, we look into the current state of research and development in environment detection, pedestrian detection, path planning, motion control, and vehicle cybersecurity for autonomous vehicles. We aim to study the different proposed technologies and compare their approaches. For a car to become fully autonomous, these technologies need to be accurate enough to gain public trust and show immense accuracy in their approach to solving these problems. Public trust and perception of auto vehicles are also explored in this paper. By discussing the opportunities as well as the obstacles of autonomous driving technology, we aim to shed light on future possibilities.
The study seeks to understand how the AI ecosystem might be implicated in a form of knowledge production which reifies particular kinds of epistemologies over others. Using text mining and thematic analysis, this paper offers a horizon scan of the key themes that have emerged over the past few years during the AIEd debate. We begin with a discussion of the tools we used to experiment with digital methods for data collection and analysis. This paper then examines how AI in education systems are being conceived, hyped, and potentially deployed into global education contexts. Findings are categorised into three themes in the discourse: (1) geopolitical dominance through education and technological innovation; (2) creation and expansion of market niches, and (3) managing narratives, perceptions, and norms.
Background
Robot-mediated interventions show promise in supporting the development of children on the autism spectrum.
Objectives
In this systematic review and meta-analysis, we summarize key features of available evidence on robot-interventions for children and young people on the autism spectrum aged up to 18 years old, as well as consider their efficacy for specific domains of learning.
Data sources
PubMed, Scopus, EBSCOhost, Google Scholar, Cochrane Library, ACM Digital Library, and IEEE Xplore. Grey literature was also searched using PsycExtra, OpenGrey, British Library EThOS, and the British Library Catalogue. Databases were searched from inception until April (6th) 2021.
Synthesis methods
Searches undertaken across seven databases yielded 2145 articles. Forty studies met our review inclusion criteria of which 17 were randomized control trials. The methodological quality of studies was conducted with the Quality Assessment Tool for Quantitative Studies. A narrative synthesis summarised the findings. A meta-analysis was conducted with 12 RCTs.
Results
Most interventions used humanoid (67%) robotic platforms, were predominantly based in clinics (37%) followed home, schools and laboratory (17% respectively) environments and targeted at improving social and communication skills (77%). Focusing on the most common outcomes, a random effects meta-analysis of RCTs showed that robot-mediated interventions significantly improved social functioning (g = 0.35 [95%CI 0.09 to 0.61; k = 7). By contrast, robots did not improve emotional (g = 0.63 [95%CI -1.43 to 2.69]; k = 2) or motor outcomes (g = -0.10 [95%CI -1.08 to 0.89]; k = 3), but the numbers of trials were very small. Meta-regression revealed that age accounted for almost one-third of the variance in effect sizes, with greater benefits being found in younger children.
Conclusions
Overall, our findings support the use of robot-mediated interventions for autistic children and youth, and we propose several recommendations for future research to aid learning and enhance implementation in everyday settings.
PROSPERO registration
Our methods were preregistered in the PROSPERO database (CRD42019148981).
There is a nationwide shortage of special education teachers (SETs) due, in part, to unmanageable workload expectations, which has reached crisis level. SETs are expected to modify, adapt, and accommodate general education curriculum to meet the needs of their students, communicate and collaborate with parents and general education teachers, and progress monitor on IEP goals, to name a few. SETs, especially those in more restrictive self-contained settings, report spending almost half of their time completing non-teaching tasks. One emerging and innovative solution to help SETs accomplish these tasks is using Artificial Intelligence (AI). AI powers many popular educational tools, such as predictive text, adaptive learning platforms, and digital assistants. The launch of ChatGPT, Bard, and other generative pre-trained transformers (GPTs), provides an opportunity to support SETs with some of the paperwork requirements. This is due to the GPTs ability to craft human-like responses via drafting essays, emails, lists, and the like. In this article, we provide step-by-step directions to use ChatGPT. Additionally, we illustrate how GPTs can be used to operationalize, automate, and streamline many of the SET’s non-teaching tasks through specific examples of its use in (1) collaboration, (2) adapting readings, and (3) developing progress monitoring assessments.
The Voiceitt app is designed for people with dysarthric speech, to support vocal communication and access to voice-driven technologies. Sixty-six participants were recruited to test the Voiceitt app and share feedback with developers. Most had physical, sensory, or cognitive impairments in addition to atypical speech. The project team liaised with individuals, their families and local support teams to provide access to the app and associated equipment. Testing was user-led, with participants asked to identify and test use cases most relevant to their daily lives over three months or more. Ongoing technical support and training were provided remotely and in-person throughout their testing. Semi-structured interviews were used to collect feedback on users' experiences, with delivery adapted to individuals' needs and preferences. Informal feedback was collected through ongoing contact between participants, their families and support teams and the project team. User feedback has led to improvements to the user interface and functionality, including faster voice training, simplified navigation, the introduction of game-style features and of switch access as an alternative to touchscreen access. This work offers a case-study in meaningful engagement with diverse disabled users of assistive technology in commercial software development.
In disease detection, generative models for data augmentation offer a potential solution to the challenges posed by limited high-quality electroencephalogram (EEG) data. The study proposes a temporal-spatial feature-aware denoising diffusion probabilistic model (DDPM), termed TF-DDPM, as an EEG time-series augmentation framework for autism research. The module for predicting noise is CCA-UNet based on the channel correlation-based attention (CCA) mechanism, which considers the spatial and temporal correlation between channels, and uses depthwise separable convolution instead of traditional convolution, thereby suppressing the interference from irrelevant channels. Visualization and binary classification results on synthetic signals indicate that proposed method generates higher quality synthetic data compared to Generative Adversarial Networks (GAN) and DDPM.
People living with disabilities can significantly enhance their level of independence and improve their quality of life through the use of assistive technology. Recent years have seen a remarkable rise in the implementation of artificial intelligence (AI) into assistive technologies which has created novel prospects for advanced assistance and self-reliance. A comprehensive exploration of AI-based assistive technology research for individuals with disabilities is presented in this paper. Important breakthroughs are highlighted along with probable areas of further advancements. We employed bibliometrix R-tool to develop analysis while also obtaining clean metadata from Scopus database. A closer examination revealed significant emerging themes in the said topic with a clear bifurcation with basic, motor and niche themes with respect to the area of study. Interaction is a key factor in collaborations between countries and reveals the clusters emerging from the bibliometric analysis. All signs could very well point towards a forthcoming consolidation of said subject matter. In summary, it appears that there remains a need for further research in order to strengthen the field of AI-based assisted technology helping those who are disabled. While current body of literature touches on accessibility, aspects of commercialization, including affordability and availability, may be taken up for further research. The finding can be helpful in future research and related fields as the article provides a global view of research production over time. However, a systematic Literature review (SLR) might help in addressing the unique needs of person with disability which remain to be unaddressed by the current literature available.
Advancement in information technology continues to evolve especially in the field of artificial intelligence (AI). Research studies have been conducted to evaluate the perceptions of Americans on the development and utilization of AI technology and if it is appropriate to use AI in public administrative duties. The research revealed that society is fragmented regarding the acceptance of AI, and whether AI decisions could have long‐term effects on the labor industry, legal system, and national security. The 2018 AI Public Opinion Survey revealed significant concerns among the American public regarding AI, yet also a recognition of its promise. The goal of this article is to further develop a governance framework for AI that considers the importance of public trust in AI policy. First, it discusses the necessity of public trust for the effective governance of emergent technology. Then, it evaluates public opinion on AI technology that specifically pertains to governance. The article concludes with a discussion of why public trust is central to good AI governance.