Dialogical Learning Support in RAG-Based E-Learning

The integration of Artificial Intelligence (AI) technologies into educational environments—particularly through Adaptive Learning Systems (ALS)—has fundamentally transformed the delivery of instruction and personalized learning. These intelligent systems leverage machine learning algorithms to continuously analyze learner data and adapt content, pacing, and pedagogical strategies to individual student needs, leading to enhanced engagement and improved learning outcomes. However, this rapid digital evolution has surfaced critical concerns about the opacity, fairness, and accountability of AI-driven decisions. Often referred to as “black-box” systems, many AI models offer little to no insight into how they arrive at recommendations or interventions, thereby posing challenges related to transparency, interpretability, trust, and ethical governance. This paper argues that to responsibly integrate AI in education, especially in contexts where student data and futures are at stake, it is essential to move beyond system performance and embrace Explainable AI (XAI)—a design philosophy and technical paradigm focused on making AI decision-making understandable to humans. We propose a student-centric framework for XAI in ALS, which places learners and educators at the center of AI design and deployment processes. The framework is informed by interdisciplinary literature and practical case studies in educational technology and AI ethics. It emphasizes three foundational pillars: (1) Human-centered design, which ensures that AI interfaces and feedback mechanisms are accessible and usable by students and educators alike; (2) Stakeholder collaboration, which fosters ongoing dialogue between developers, teachers, learners, ethicists, and policymakers; and (3) Ethical integration, which embeds principles such as fairness, inclusivity, data privacy, and algorithmic accountability into the development lifecycle of adaptive learning tools. The framework also outlines actionable strategies to ensure that explanations provided by AI systems are contextually meaningful, developmentally appropriate, and pedagogically aligned. It considers the differentiated cognitive needs of various learner groups and promotes educational transparency as a right, not a privilege. Ultimately, this student-centric XAI framework aims to cultivate trustworthy, ethical, and pedagogically sound AI systems that not only support learning but also uphold the rights, agency, and dignity of learners in the digital age. By advocating for transparency and ethical accountability, this work contributes to the growing body of research on responsible AI in education and offers practical guidance for institutions aiming to deploy adaptive technologies in equitable and explainable ways.

"Background: Artificial Intelligence (AI) has become a ubiquitous phenomenon in recent times, with most organizations today attempting to maneuver their way around developing AI systems with the aim of improving the products and services they provide. However, what complicates developing AI systems is the paucity in frameworks to support organizations with AI System Development (SD). As a result, many organizations are using existing approaches which have been previously applied in earlier emerging Information Technology endeavors. This study explored how a framework can promote effective organizational AI SD. To achieve this a holistic framework for AI SD was conceptualized and examined from an organizational perspective. Method: This study has examined the conceptualized framework People-Process-Data-Technology (2PDT) in AI, through a case study research design. The empirical data was analyzed based on 12 case studies within Australia including 39 interviews with AI experts. We have applied thematic analysis to investigate requirements of organizational AI SD. Results: The results demonstrate that organizations are challenged by key factors, which inhibits their ability to effectively develop AI systems. For example, organizations are not achieving successful delivery of AI systems due to a lack of required skills. Additionally, a plethora of AI technology which is constantly evolving, poor data quality, and the paucity of AI SD frameworks are all contributing to unpredictable delivery outcomes. Conclusion: This paper investigated requirements for effective organizational AI SD by examining the 2PDT framework. The results contribute to AI phenomenon by developing the requirements for AI SD, in terms of people, process, data and technology. It contributes to theory by evaluating and developing AI requirements for effective AI systems. The examination of the framework and case study approach added valuable knowledge to the AI domain. In addition, we contributed to practice by identifying requirements that organizations should consider in achieving better AI SD outcomes."

This chapter explores relationships between the science and profession of psychology and the development of artificial intelligence (AI) and robotic systems. It briefly examines the role of psychology in the development of AI systems and theories. There is a long and intimate history of such a relationship, exemplified in the arts and the social sciences as well as in computer science and engineering. The chapter examines the role that psychology has played in the understanding of the ways in which humans interact with contemporary AI systems, to influence the behaviour of the systems and, in turn, to be influenced by them. Human decision making has been shown to be improved by factors other than intelligence and thereby appears to be a quality which would be retained by humans rather than machines, for example motivational processes, emotions and experience. The chapter examines the state of the profession of psychology and helping professions in the light of developments in AI.

The development of the digital revolution facilitates innovative models that generate new markets and business opportunities. The reappearance of artificial intelligence (AI) has created further potentials and types of market participation. AI is understood as a cutting-edge technology and a key driver of the transition of our economy into the digital economy. It is important to recognize and constantly bear in mind that artificial intelligence systems provide certain benefits but are associated with certain risks and potential negative effects. The European Commission, in its Ethical Guidelines for Trustworthy Artificial Intelligence (2019), emphasizes ethical principles and associated values that must be respected in the development, introduction, and use of artificial intelligence systems: respect for human autonomy, prevention of harm, fairness, and explainability. The question arises as to whether the emerging fundamental ethical principles and regulatory policies concerning AI systems require certain adaptations when it comes to the application of AI technology in connection with the sustainable development goals. The development of artificial intelligence systems compatible with the goals of sustainable development, as defined in the 1987 report of the UN Brundtland Commission as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs”, is encouraged. Sustainability or sustainable development is defined in the literature as a concept based on three pillars - encompassing social, economic, and environmental aspects. The European Commission highlights that sustainable development is a fundamental principle of the Union Treaty and a priority goal of Union policies, along with digitization and a robust single market. In recent years, doctrine has sought to bridge the gap between the two disciplines by introducing the term “Sustainable AI”. The aim of the paper is to understand the development of AI that is compatible with sustainable goals. To understand this, it is necessary to comprehend the basic concepts of artificial intelligence systems and explore the sociological, ecological, and economic implications of these systems, all with the aim of finding ways to achieve the goals of sustainable development and the sustainability of AI systems themselves. These are closely tied to adhering to the highest ethical principles with the responsible use of artificial intelligence systems.

As artificial intelligence (AI) becomes increasingly integral to our lives, ensuring these systems are trustworthy and transparent is paramount. The concept of explainability has emerged as a crucial element in fostering trust within AI systems. Nevertheless, the dynamics between explainability and trust in AI are intricate and not fully comprehended. This paper delves into the nexus between explainability and trust in AI, offering perspectives on crafting AI systems that users can rely on. Through an examination of existing literature, we investigate how transparency, accountability, and human oversight influence trust in AI systems and assess how various explainability approaches contribute to trust enhancement. Utilizing a set of experiments, our research examines how different explanatory models impact users' trust in AI systems, revealing that the nature and quality of explanations have a significant influence on trust levels. Additionally, we scrutinize the balance between explainability and accuracy in AI systems, discussing its implications for the development of reliable AI. This study underscores the critical role of explainability in engendering trust in AI systems, providing guidance on the development of AI systems that are both transparent and trustworthy, thereby fostering confidence among users.

Artificial intelligence technologies are developing intensively today, including due to the development of technologies of stable neural networks and cloud computing infrastructures, fuzzy system technologies, entropy administration, swarm intelligence, evolutionary computations, and many others. etc. At the same time, the problem of almost complete absence of normative legal regulation and normative technical regulation of the foundations, conditions and features of development, functioning and activities, integration into other systems and control over the use of artificial intelligence technologies is global today. The Article is devoted to the study of the specifics of the legal regulation of the use and development of artificial intelligence. Some approaches to the definition of artificial intelligence and the features of legal regulation of this sphere, which take place in the scientific literature, are considered and the author's definition of artificial intelligence is developed and given through the disclosure of its main features. In particular, according to the proposed definition, artificial intelligence is an artificial complex cybernetic computer-software-hardware system possessing the properties of substantivity, autonomy, and also the ability to perceive and analyze data, to self-learn. The question of positioning the artificial intelligence system as a special form of person (for example, the so-called «electronic person»), that is, granting it a certain legal personality depending on various factors and the field of application of such a system, is considered in this article. The article also notes the main possible approaches to legal support for the use and development of artificial intelligence systems, in particular, such approaches include the preemptive universal-total legal regulation and legal regulation aimed at the regulation of specific artificial intelligence systems. The main risks and uncertainties associated with artificial intelligence (and which are of great importance for the adoption of legislation in this field) have been investigated. There are drawn the conclusions about how to develop legislation on the use and development of artificial intelligence: consistently, taking into account the specifics of areas of its application, as well as balancing the interests of individuals, society and the state with regard to proper security and protection of individual rights, and interests related to the development of innovation for the benefit of the whole society.

Artificial intelligence (AI) systems are increasingly being used in consumer as well and business applications. Companies are using them, Governments are using them, public security, medical researchers, you and I – everybody seems to be somehow touched. As it becomes more advanced and ubiquitous in our lives, it is critical that AI developers and users understand the ethical implications and challenges posed by this powerful technology. This paper explores the key ethical skills and considerations that are essential for navigating the new world of AI. Artificial Intelligence (AI) presents significant ethical dimensions across various sectors, including research, business, and daily life. It is important to appreciate the major ethical dimensions of AI and how they impact AI systems. Policies and guidelines needs to be laid out for development and usage of such systems. Automated Decisions/Responsibility: No matter how intelligent a AI system might perceived to be, it is always prone to making errors. So, AI system that make automated decisions will always be dangerous. Therefore, it is important to ensure that there is always "human intervention" or "human review" for outcomes/decisions made by AI systems. Determining accountability for AI-generated actions or content remains a significant challenge, emphasizing the need for ethical governance frameworks. There has to be human accountability.Fairness/Bias: AI systems can perpetuate existing biases present in training data, leading to discriminatory outcomes.Transparency: The opaque nature of many AI algorithms complicates accountability, making it difficult to understand decision-making processes.Privacy: AI's capability to process vast datasets can infringe on individual privacy rights, necessitating stringent data protection measuresHuman Dignity: The risk of AI-generated content resembling existing works raises ethical issues regarding originality and authorship.

The article examines the current state of legal regulation of the use of artificial intelligence technologies in the education system with a focus on protecting the rights of students, teachers and other participants of the educational process. In the context of global digitalization and active implementation of intelligent technologies, the issues of ensuring transparency of algorithms, protection of personal data, and guaranteeing non-discriminatory access to educational services and resources are of particular relevance. Artificial intelligence opens up new opportunities in education: from personalizing curricula and adaptive learning to automating knowledge assessment, introducing virtual and augmented reality, expanding the possibilities of inclusive education, and big data analytics for effective management of educational institutions. Such innovations help to improve the quality of education, promptly identify problem areas in the learning process, and improve interaction between participants in the educational environment. At the same time, they require increased attention from the state in terms of legal guarantees, transparency of decision-making by artificial intelligence systems, and respect for basic human rights. The author analyzes Ukrainian legislation and international legal acts, in particular the General Data Protection Regulation (GDPR), the European Artificial Intelligence Act (AI Act), UNESCO recommendations and other strategic documents that define approaches to the regulation of digital technologies. It is noted that the legal mechanisms available in Ukraine do not yet fully take into account the specifics of the impact of artificial intelligence on the educational environment, which necessitates a comprehensive modernization of legal regulation with due regard for international experience. The authors emphasize the need to adapt the best international practices, develop ethical standards, introduce mandatory audit of algorithms, and establish independent institutions for the protection of the rights of participants in the educational process. It is concluded that only an effective, modern and balanced legal policy can guarantee the safe, ethical and fair use of artificial intelligence in the educational sphere.

In response to Li, Reigh, He, and Miller's commentary, Can we and should we use artificial intelligence for formative assessment in science, we argue that artificial intelligence (AI) is already being widely employed in formative assessment across various educational contexts. While agreeing with Li et al.'s call for further studies on equity issues related to AI, we emphasize the need for science educators to adapt to the AI revolution that has outpaced the research community. We challenge the somewhat restrictive view of formative assessment presented by Li et al., highlighting the significant contributions of AI in providing formative feedback to students, assisting teachers in assessment practices, and aiding in instructional decisions. We contend that AI‐generated scores should not be equated with the entirety of formative assessment practice; no single assessment tool can capture all aspects of student thinking and backgrounds. We address concerns raised by Li et al. regarding AI bias and emphasize the importance of empirical testing and evidence‐based arguments in referring to bias. We assert that AI‐based formative assessment does not necessarily lead to inequity and can, in fact, contribute to more equitable educational experiences. Furthermore, we discuss how AI can facilitate the diversification of representational modalities in assessment practices and highlight the potential benefits of AI in saving teachers’ time and providing them with valuable assessment information. We call for a shift in perspective, from viewing AI as a problem to be solved to recognizing its potential as a collaborative tool in education. We emphasize the need for future research to focus on the effective integration of AI in classrooms, teacher education, and the development of AI systems that can adapt to diverse teaching and learning contexts. We conclude by underlining the importance of addressing AI bias, understanding its implications, and developing guidelines for best practices in AI‐based formative assessment.

As the AI (Artificial Intelligence, AI) technology, especially the language model and the rapid development of intelligent learning system in the field of language education, contextualized input and output have gradually become an important path to promote Second Language Acquisition (SLA). Based on the theory of input hypothesis and output hypothesis and social culture and interactive approaches, this article system combed the AI drive learning environment, language input, output, and the change of the interactive mechanism and its influence on second language acquisition. Research suggests that AI can enhance the understandability and relevance of input through adaptive recommendation, multimodal presentation, and virtual situation simulation. At the output level, AI dialogue system, real-time error correction and scaffolding support for language learners provides a high frequency, low anxiety generated opportunities, complexity and accuracy in grammar, syntax and vocabulary, expression fluency and pragmatic appropriateness, etc, to promote the language output quality improvement. More importantly, AI integrates input, output and feedback into a dynamic loop process, thereby reshaping the occurrence conditions of traditional second language acquisition to a certain extent. In the affirmation of the potential of the AI in language learning at the same time, the paper also points out its authenticity, learner differences in interactive adaptation and technology rely on is still limited, and emphasizes on the importance of teachers' professional guidance to realize man-machine coordinated. This article aims to provide references for the research and instructional design of second language teaching supported by AI from both theoretical and practical perspectives.

Comprehensive chart documentation is a key competency in dental hygiene education. Providers spend a significant portion of their day on record-keeping, over 12% in primary care settings [1]. Accurate charting supports decisions, billing, and legal protection, but excessive documentation reduces time for patient education. It also limits communication, leadership, and collaboration by restricting feedback and team interaction. At the same time, dental education must adapt to emerging artificial intelligence (AI) tools designed to improve efficiency and support learning. AI-assisted charting and automated note generation show promise in reducing documentation time [2, 3]. Despite the growing interest in AI, little is known about how such systems can be effectively integrated into dental hygiene training to enhance both perceived efficiency and patient communication [4]. Students continue to learn traditional, manual charting methods. AI-based documentation tools can help create record periodontal charts, clinical notes, analyze data, and improve record accuracy [5]. The lack of such training limits students’ and teachers’ awareness and readiness to use AI systems. To address these challenges, a dental hygiene program piloted DentalBee, an AI-powered tool that converts clinical speech into structured dental notes. The project aimed to determine whether AI-assisted documentation could improve workflow efficiency and patient engagement while integrating digital tools into dental hygiene education. The pilot involved 2nd-year students during clinical rotations and included faculty training, AI-assisted modules, and integration with the patient management system (PMS) (Table 1). Launched in Spring 2025, the initiative received administrative and IT support. Outcome measures focused on students’ perceived efficiency and patient interaction, assessed through a structured survey. Faculty also observed reduced documentation time, though exact times were not recorded because implementation occurred alongside student training, conditions that would have artificially inflated time data and misrepresented tool performance. This pilot established baseline perceptions of efficiency and engagement, providing a foundation for future studies to measure objective time savings once system use becomes routine. Institutional review board exemption was granted for this educational quality-improvement project. A quantitative survey assessed student perceptions of documentation efficiency and patient communication at three timepoints: pre-implementation (n = 23), mid-rotation (1 month; n = 23), and post-rotation (3 months; n = 20). At baseline, 74% of participants reported that documentation significantly impacted their perceived appointment efficiency (Figure 1). Following AI integration, 12.5% of students reported noticeable improvement at mid-rotation, increasing to 50% by post-rotation—suggesting a gradual adaptation and perceived benefit over time. In terms of patient communication, 50% of students initially indicated that note-taking limited their ability to connect with patients, while 27% reported a moderate burden and 23.5% a high burden (Figure 2). After using the AI system, 15.6% reported high improvement in patient connection at mid-rotation, increasing to 41.9% by post-rotation. Although self-reported, these findings suggest that with continued use, AI-assisted documentation may improve perceived workflow efficiency and facilitate more meaningful patient engagement. Future studies should include objective measurement of documentation time and direct assessment of patient outcomes to strengthen these preliminary results. The authors would like to acknowledge the support of Catherine Ford, Dean of Owens Community College School of Nursing and Health Professions. Acknowledgment to Kanza Javed for the logistic support.

Artificial intelligence (AI) is widely used in medicine. AI may provide low-cost solutions to health problems and is especially important for developing countries. Health-care professionals will play an important role in providing data for educating AI systems and validating these through clinical trials. AI may necessitate changes in the different roles of a physician and possibly other professionals. Intelligent tutoring systems can support student learning by providing individualized feedback and creating personalized learning pathways. Role-plays with an intelligent active agent can enhance students' interaction with computers and activate their sense of responsibility. AI can support personalized learning by intelligent agents, autonomous scoring, and chatbots. AI has an important role to play in supporting simulations, serious games, and the gamification of learning. Learning analytics and educational data mining are two other important applications. Personalized prediction is also an important benefit. AI will supplement the work of educators and can reduce curricular overload by migrating some knowledge to AI algorithms. Routine tasks and responses to routine queries of learners can be provided by AI. AI can support continuing professional education by incorporating longitudinal and innovative formative assessment methods that can help identify knowledge and skill gaps and support learning. The use of AI in curriculum review and assessment has been limited. Data integrity and privacy are important issues to consider. Unconscious bias in the data used to educate AI systems is also possible. Most of the literature is from developed countries and among medical students and residents.

The involvement of artificial intelligence (AI) systems in the field of education/pedagogy is an irreversible process. The segment of general secondary education is promising for the introduction of AI services. Schools must provide students with basic skills and foster AI competence to prepare them for a high-tech future. UNESCO has developed a global regulatory framework for artificial intelligence. Some countries are already demonstrating successful initiatives to integrate new technology in schools. At the same time, the implementation of AI tools in the educational environment is a legitimate concern, because it is associated with high risks. In Ukraine, the study of various aspects of integrating AI technologies into secondary education is at an early stage. The purpose of the article is to study the advantages of implementing artificial intelligence tools in the segment of general secondary education, as well as some risks that teachers should be prepared for. The study is based on an in-depth analysis of numerous domestic and, particularly, foreign publications on the topic, and draws on the existing experience of testing various AI tools by educators and the author. Special attention is paid to the potential use of AI by students to improve learning outcomes through increased motivation, self-education and mentoring, personalized and interactive learning etc. Some challenges associated with the introduction of new technology, which is gaining momentum among students, are discussed, as well as suggestions on how to avoid the negative consequences of its incorrect use. In conclusion, it is noted that AI technology can help students develop creativity, innovation, creative abilities, providing a variety of tools and resources, as well as new opportunities for project and educational research activities. With the spread of new technology in general secondary education institutions, there is a need to change approaches to learning, including revising strategies for monitoring and correcting students' knowledge, skills, and abilities. It is timely to develop a methodology for teaching with AI in the context of various educational components of the school curriculum.

With the popularization and development of the concept of artificial intelligence, the application of artificial intelligence has also begun to deepen into people's lives. While bringing convenience to people, it has also made some people worry about whether artificial intelligence will replace humans. Therefore, In order to make people understand the current development status and bottlenecks of artificial intelligence more intuitively, as well as the difference between artificial intelligence and human brain, this article will turn from speech recognition and natural language processing, human-computer dialogue, image recognition, and machine learning ability, that is, machine listening, reading, and thinking four aspects of research and discussion, and finally summarize why artificial intelligence cannot completely surpass humans.

PurposeThe purpose of this study is to critically investigate the effects of artificial intelligence (AI)-driven learning environments on learner autonomy, knowledge co-construction and digital equity in Thailand’s online education context. It aims to analyze how algorithmic learning tools and AI-based systems shape learners' educational experiences, particularly focusing on whether these technologies enhance or constrain learner autonomy and collaborative interactions. Additionally, it evaluates ethical and social implications, particularly the digital divide, algorithmic biases and the transparency of AI-mediated educational processes. The study seeks to provide actionable insights and recommendations for policymakers, educators and developers to optimize AI integration for inclusive lifelong learning.Design/methodology/approachThis research adopts a qualitative case study methodology involving in-depth, semi-structured interviews, participant observations and detailed document analysis from 30 diverse participants, including urban and rural learners, educators and instructional designers. Employing actor-network theory (ANT) and posthumanist theoretical frameworks, the study critically examines interactions between human and non-human actors within AI-driven educational environments. This approach allows for comprehensive exploration of participants' lived experiences, perceptions and interactions with AI technologies. Triangulation of multiple data sources ensures depth and reliability, providing nuanced insights into how AI systems influence learner autonomy, collaborative learning processes and educational equity.FindingsThe study identifies AI-driven learning platforms as having dual impacts on learner autonomy, simultaneously enabling greater self-directed learning through personalized guidance and real-time feedback while imposing algorithmic constraints that restrict intellectual exploration. AI-facilitated knowledge co-construction effectively organizes and structures peer interactions yet often results in depersonalized, transactional communication lacking deep human engagement. Furthermore, significant digital inequities are evident, with rural learners disproportionately disadvantaged due to limited technological infrastructure and algorithmic biases embedded within AI-driven platforms. These inequities highlight a critical need for inclusive, transparent and ethical design practices to ensure equitable access and meaningful learner agency.Research limitations/implicationsThe qualitative, context-specific nature of the case study limits the generalizability of findings beyond Thailand’s online educational environment. The rapidly evolving nature of AI technologies suggests that findings reflect a specific temporal context, necessitating continuous research updates. Participant selection bias may have influenced findings, with participants potentially holding strong pre-existing opinions on AI-driven education. Future studies should consider longitudinal analyses to understand long-term impacts, comparative cross-cultural research to validate findings and deeper exploration of algorithmic biases, fairness and ethical considerations in AI-driven educational tools, particularly how they affect diverse socio-economic groups.Practical implicationsPractically, this research underscores the necessity of designing AI-driven learning platforms that genuinely empower learner autonomy and critical thinking rather than restrict or overly dictate educational pathways. Educational practitioners and technology developers should implement transparent AI decision-making processes, enabling learners to understand and critically evaluate algorithmic recommendations. Policies must prioritize digital infrastructure expansion and digital literacy programs to reduce urban-rural digital disparities. Furthermore, pedagogical practices should integrate human-centric approaches to complement AI tools, ensuring that AI technologies serve to enhance, rather than replace, essential human interactions within collaborative learning environments.Social implicationsThe study emphasizes that AI-driven education holds significant potential for democratizing lifelong learning by offering personalized and adaptive learning experiences. However, it also warns of the risks associated with reinforcing existing socio-economic and digital inequalities, particularly between urban and rural learners. Addressing algorithmic biases and digital divides through targeted policy interventions, infrastructural investments and inclusive educational practices is critical for fostering equity and inclusivity in education. By ensuring that AI platforms are accessible and beneficial for all learner demographics, societies can leverage technology as a tool for broad-based educational empowerment and social development rather than a mechanism perpetuating exclusion.Originality/valueThis research contributes original insights into the nuanced impacts of AI-driven learning on learner autonomy, collaborative interactions and digital equity within the Thai educational context – areas relatively underexplored in existing literature. By uniquely integrating actor-network theory and posthumanist perspectives, it offers a sophisticated understanding of the interplay between humans and technologies in educational settings. The empirical findings deliver crucial evidence-based recommendations for educators, policymakers and developers aiming to ethically and inclusively leverage AI in education. Ultimately, the study enriches ongoing discussions on balancing technological advancement with human-centric, equitable and socially responsible educational practices.