Positive Artificial Intelligence in Education (P-AIED): A Roadmap

Bittencourt, Ig Ibert; Chalco, Geiser; Santos, Jário; Fernandes, Sheyla Christine Santos; Silva, Jesana; Batista, Natanael Silva; Hutz, Cláudio Simon; Isotani, Seiji

doi:10.1007/s40593-023-00357-y

Cited by 14 publications

(6 citation statements)

References 293 publications

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“…It is also important to consider the assessment context (e.g., formative vs. summative, group-vs. individual-level reporting) and implications for measurement (e.g., comparability, scoring, interpretation) when determining how best to apply CA in practice. CA introduces opportunities to enhance students' assessment experiences and to advance use of assessment outcomes to further individuals' educational opportunities and wellbeing (Bittencourt et al, 2023). However, effective design and implementation of personalized assessments is a complex endeavor, which may necessitate new processes for designing assessments (O'Dwyer et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

“…For example, student engagement has been utilized in effort-monitoring computer-based tests (Wise et al, , 2019 and a wider range of student emotions have been used to enhance performance-based adaptation in several personalized learning systems Forbes-Riley and Litman, 2011). Research in the field of artificial intelligence in education (AIEd) has increasingly emphasized a more holistic picture of learners which takes into account cognitive, metacognitive, and affective aspects of the learner to explain their behavior in learning environments (Grafsgaard et al, 2012;Kizilcec et al, 2017;Yadegaridehkordi et al, 2019), reflecting growing interest in integrating positive psychology into research within the AIEd community (Bittencourt et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

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Caring assessments: challenges and opportunities

Sparks,

Lehman,

Zapata-Rivera

2024

Front. Educ.

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Caring assessments is an assessment design framework that considers the learner as a whole and can be used to design assessment opportunities that learners find engaging and appropriate for demonstrating what they know and can do. This framework considers learners’ cognitive, meta-cognitive, intra-and inter-personal skills, aspects of the learning context, and cultural and linguistic backgrounds as ways to adapt assessments. Extending previous work on intelligent tutoring systems that “care” from the field of artificial intelligence in education (AIEd), this framework can inform research and development of personalized and socioculturally responsive assessments that support students’ needs. In this article, we (a) describe the caring assessment framework and its unique contributions to the field, (b) summarize current and emerging research on caring assessments related to students’ emotions, individual differences, and cultural contexts, and (c) discuss challenges and opportunities for future research on caring assessments in the service of developing and implementing personalized and socioculturally responsive interactive digital assessments.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Caring assessments: challenges and opportunities

Sparks,

Lehman,

Zapata-Rivera

2024

Front. Educ.

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“…[69] 2024 The applications of Artificial Intelligence in Education have emerged as useful assets for educators, exhibiting a notable influence on a variety of aspects of teaching and learning. The use of these technologies has the potential to improve pedagogical decision making as well as instructional methods [15,[79][80][81]. The effective synergy between educators and AI tools results in several benefits, one of which is the capability to compare the decisions made by teachers with the resources provided by AI [10,15,82].…”

Section: Title Yearmentioning

confidence: 99%

“…Despite AI's promising contributions to educators, its role should be recognized as supportive rather than substitutive, ensuring that teachers remain central to the learning process [90,91]. Many studies show that teacher replacement is one of the biggest fears when considering AI adoption [15,80,82,[91][92][93][94]. Consequently, AIED adoption necessitates considering teachers' perceptions and readiness, facilitating effective implementation within higher education systems.…”

Section: Title Yearmentioning

confidence: 99%

A Social Perspective on AI in the Higher Education System: A Semisystematic Literature Review

Alshahrani,

Pileggi,

Karimi

2024

Electronics

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The application of Artificial Intelligence in Education (AIED) is experiencing widespread interest among students, educators, researchers, and policymakers. AIED is expected, among other things, to enhance learning environments in the higher education system. However, in line with the general trends, there are also increasing concerns about possible negative and collateral effects. The consequent social impact cannot be currently assessed in depth. Balancing benefits with social considerations according to a socio-technical approach is essential for harnessing the true power of AI in a responsible and trustworthy context. This study proposes a semi-systematic literature review of the available knowledge on the adoption of artificial intelligence (AI) in the higher education system. It presents a stakeholder-centric analysis to explore multiple perspectives, including pedagogical, managerial, technological, governmental, external, and social ones. The main goal is to identify and discuss major gaps and challenges in context, looking at the existing body of knowledge and momentum. AIED should encompass pedagogical, ethical, and social dimensions to be properly addressed. This review highlights a not-always-explicit socio-technical perspective. Additionally, this study reveals a significant lack of empirical systematic evaluation of added value and institutional readiness. Because of the broad scope of the study and the intense ongoing debate on the topic, an exhaustive identification of the current body of knowledge is probably unrealistic, so this study aims mainly to identify the mainstream and major trends by looking at the most recent contributions.

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“…Despite recent interest in AI in teaching and learning [12], there may still be areas that have received limited attention in the academic literature. Some educational domains where AI hasn't gotten much attention in the scholarly literature include: emotional intelligence [13], the ability to promote kindness and empathy [14,15] and the wellbeing of students, teachers, and other educational stakeholders [16], student creative thinking [17], cultural sensitivity and diversity in education [18], gender issues arising when using ChatGPT [19] or gender-based violence [20], physical education and sports training through personalized coaching and performance analysis [21], special educational needs in personalized learning, communication, and skill development [22], etc. A bibliometric analysis can help identify knowledge gaps and underrepresented topics, guiding future research directions and potential areas of exploration.…”

Section: Introductionmentioning

confidence: 99%

Unveiling Insights: A Bibliometric Analysis of Artificial Intelligence in Teaching

Ivanova,

Grosseck,

Holotescu

2024

Informatics

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The penetration of intelligent applications in education is rapidly increasing, posing a number of questions of a different nature to the educational community. This paper is coming to analyze and outline the influence of artificial intelligence (AI) on teaching practice which is an essential problem considering its growing utilization and pervasion on a global scale. A bibliometric approach is applied to outdraw the “big picture” considering gathered bibliographic data from scientific databases Scopus and Web of Science. Data on relevant publications matching the query “artificial intelligence and teaching” over the past 5 years have been researched and processed through Biblioshiny in R environment in order to establish a descriptive structure of the scientific production, to determine the impact of scientific publications, to trace collaboration patterns and to identify key research areas and emerging trends. The results point out the growth in scientific production lately that is an indicator of increased interest in the investigated topic by researchers who mainly work in collaborative teams as some of them are from different countries and institutions. The identified key research areas include techniques used in educational applications, such as artificial intelligence, machine learning, and deep learning. Additionally, there is a focus on applicable technologies like ChatGPT, learning analytics, and virtual reality. The research also explores the context of application for these techniques and technologies in various educational settings, including teaching, higher education, active learning, e-learning, and online learning. Based on our findings, the trending research topics can be encapsulated by terms such as ChatGPT, chatbots, AI, generative AI, machine learning, emotion recognition, large language models, convolutional neural networks, and decision theory. These findings offer valuable insights into the current landscape of research interests in the field.

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Positive Artificial Intelligence in Education (P-AIED): A Roadmap

Cited by 14 publications

References 293 publications

Caring assessments: challenges and opportunities

Caring assessments: challenges and opportunities

A Social Perspective on AI in the Higher Education System: A Semisystematic Literature Review

Unveiling Insights: A Bibliometric Analysis of Artificial Intelligence in Teaching

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