Open access peer-reviewed chapter
The ethics of AI, as suggested by UNESCO.
Abstract
Artificial intelligence (AI) has arisen as a transformative technology with the capacity to revolutionize education. When integrated into educational settings, AI systems can transform teaching methods, increase student engagement, and create personalized learning environments. Therefore, incorporating AI applications into K-12 education can improve learning and teaching outcomes by making instruction more personalized. Despite these advantages, integrating AI into education raises ethical questions and considerations that need to be addressed to implement it responsibly and effectively. This study examines the critical ethical concerns associated with integrating AI into K-12 schools. Specifically, it addresses two key questions: First, what ethical challenges arise from using AI in this educational context? Second, which ethical principles should be followed to effectively address and mitigate these challenges? The study emphasizes the importance of creating a framework for the ethical use of AI that protects students’ rights and promotes equitable educational opportunities.
Keywords
- artificial intelligence
- AI ethics
- ethical implications
- ethical principles
- K-12 education
1. Introduction
AI involves computer systems. These systems are based on algorithms that can perform tasks typically associated with human intelligence. They intertwine human-like reasoning with logical, performance-driven behaviors. AI: a modern approach, third edition [1]. It is a quickly changing field that has the potential to change many aspects of life, including education.
AI technology is used in education beyond traditional classrooms, including online and distance learning environments. Research conducted in various global contexts has revealed a consensus on the educational benefits of AI.
For instance, scholars [2, 3, 4] have shown that AI can significantly benefit education by facilitating personalized and effective learning. They claim that AI can assist teachers with instructional duties, such as reviewing topics and answering student questions. Moreover, Chen et al. [5] suggest that AI can promote global learning, optimize content distribution, and improve educational management. Furthermore, Pedro et al. [4] emphasizes that AI provides learning opportunities for those excluded from education due to illness, disability, child labor, or refugee status. Therefore, integrating AI into education is crucial for improving the quality of teaching and learning, as well as shaping the future of education.
Almost every country has implemented AI curricula in K-12 schools except for Armenia, Austria, Belgium, India, Serbia, South Korea, Kuwait, and Jordan. Specifically, Qatar offers a Computing and Information Technology program averaging over 200 hours per year. Belgium offers an IT Repository program averaging over 200 hours per year. China has integrated AI into its high school curriculum, emphasizing programming, machine learning, and decision-making. Indonesia’s government has focused on inclusive policies to enhance education and learning using AI [6]. Schools and governments worldwide are exploring ways to integrate AI into their societies, resulting in a variety of emerging applications. Different socioeconomic contexts, cultural attitudes, and educational systems shape these uses. For example, Vietnam has recognized the potential of AI-powered adaptive platforms to improve students’ learning effectiveness. Legislative regulations are being examined to adapt to technological advancements in education [6, 7]. In India, the Central Board of Secondary Education has begun implementing AI into the curriculum as part of the National Education Policy [8]. As part of a broader strategy to establish itself as a digital and educational innovation leader in the Africa-Europe-Mediterranean area, Morocco implemented AI-driven educational reforms [9].
Integrating AI into education has the potential to transform learning and teaching processes and practices. However, it is crucial to address the ethical considerations associated with AI, such as responsibility issues, agency challenges, and surveillance risks. This chapter discusses the ethical challenges associated with integrating AI into K-12 schools.
2. AI in K-12 schools
Incorporating AI into K-12 education offers many advantages, including increased accessibility, personalized learning opportunities, and innovative teaching methods. According to the literature [5, 10, 11, 12, 13], incorporating AI technologies into educational settings – spanning early childhood education, physical education, and STEM disciplines – has been associated with increased student engagement and improved knowledge retention. This prepares learners to tackle the complexities of twenty-first-century challenges. Wu et al. [14] found that using AI technology improves students’ abilities and understanding in China. Moreover, Ma et al. [15] found that the use of AI moderated the connection between behavioral problems and learning adaptability. Their findings revealed that specifically, the positive influence of learning adaptability on behavioral issues increased with AI usage. This indicates that frequent AI use can amplify its impact on behavioral outcomes.
Teachers in several countries use AI for various purposes in K-12 schools, including providing personalized learning, offering real-time feedback, designing curricula, generating test questions, and evaluating assignments [16]. Specifically, in Spain, teachers in K-12 schools are open to using AI, although few have incorporated AI-based tools into their teaching methods. These teachers primarily use AI to create content, such as presentations, games, texts, and videos [17]. Besides, in Estonia, teachers use AI to address the needs of diverse learners, provide feedback, track student progress, and perform administrative tasks [18]. Similarly, teachers in China use AI to track student progress, analyze data, evaluate students, perform administrative tasks, and enhance teaching methods [19]. Furthermore, math teachers in Finland use AI to enhance their instruction, including performing calculations, producing graphs and diagrams, providing formative assessments, supporting understanding of concepts and connections, and adding variety to the teaching and learning process [20]. To conclude, AI systems provide personalized instruction, track and support individual needs of students, and offer 24/7 support through chatbots and virtual tutors. These features can greatly enhance student participation, understanding, and academic achievement. Figure 1 shows possible uses of AI in K-12 schools.
Figure 1.
Possible usage of AI in K-12 education.
However, the integration of AI within education is not without its drawbacks. The use of AI in educational contexts is fraught with fundamental risks, including concerns over data privacy, data utilization, algorithmic bias, and the potential ramifications for assessment practices [21]. These risks can result in ethical challenges such as a lack of critical thinking, deepening inequalities, laziness, and plagiarism. Additionally, transitioning to AI-enhanced educational environments requires substantial investments in infrastructure, extensive training, and ongoing support. These investments raise significant ethical considerations, such as ensuring equal access to these systems, especially in rural areas [6, 22, 23, 24].
Recent discourse has aimed to elucidate strategies for leveraging AI to enhance both the effectiveness and efficiency of education, with various stakeholders probing the ethical dimensions of these advancements. Scholars (e.g., [25, 26]) have identified the ethical challenges inherent in the development and management of AI systems, advocating for the establishment of guiding ethical principles in the application of AI within educational contexts. For instance, Zawacki-Richter et al. [27] underscored the need to establish robust theoretical and pedagogical foundations to ensure that AI applications in education are both educationally and ethically sound. Besides, Lai et al. [28] emphasized the importance of using AI technologies ethically and responsibly to avoid negative consequences for students’ social and emotional development. In addition, the possibility of AI replacing human educators raised ethical concerns [23] about how the educational environment might change.
As highlighted by scholars [23, 29, 30], it is crucial to embed ethical considerations into the development and implementation of AI systems. The examination of these ethical implications remains a pivotal aspect of the ongoing dialog surrounding AI in education.
3. Ethical challenges of using AI in education
Integrating AI into education raises numerous ethical concerns that warrant careful consideration. Key issues include privacy, security, data utilization, the biases inherent in AI systems, and the potential impact of these systems on assessments [21]. Initially, AI bias is a significant concern because these systems often produce inconsistent or unfair results that mirror and perpetuate existing societal biases [31]. These biases can influence the development of AI, exacerbate existing societal prejudices and result in unequal educational outcomes.
3.1 Biases
Inherent biases throughout the AI pipeline include algorithmic, data, confirmation, automation, accessibility, cognitive, and transparency biases. Addressing these ethical considerations is critical to ensuring that AI enhances educational equity rather than undermines it.
Algorithmic Bias : Algorithmic bias refers to the phenomenon where the design and function of an algorithm lead to outcomes that systematically disadvantage one group while favoring another. This issue is particularly pertinent in the domain of AI, which often relies on extensive datasets that may reflect pre-existing societal biases. AI systems trained on such datasets can inadvertently perpetuate stereotypes and reinforce existing inequalities [32, 33, 34, 35, 36]. The ramifications of algorithmic bias are particularly concerning in educational settings, where it can lead to unjust and unreliable decision-making processes. For instance, certain student populations may experience discriminatory outcomes due to biased algorithms, which jeopardize the integrity of educational assessments and reinforce negative stereotypes. Such biases can adversely affect students’ self-esteem and academic aspirations [22, 24, 37, 38]. Therefore, developing algorithms that consider diversity and equity is crucial [39].Data Bias : This category includes biases in data collection, data preparation, and data annotation. Data collection can be biased in a number of ways. Some of these include population, sampling, selection, representation, and exclusion biases, which are caused by human factors. Other biases are related to machine bias and include data generation, missing data, data imputation, and visualization [39, 40]. Data bias is caused by the training data used to develop AI systems. If these datasets are not diverse or representative enough, they can lead to biased decision-making processes that perpetuate existing societal biases [41]. AI models trained on data that is not representative of the diverse student population may produce biased predictions and recommendations. This is particularly concerning because biases in educational data can have significant societal consequences [36].Confirmation and Automation Bias : Educators and administrators may trust the biased or inaccurate outputs of AI systems because they tend to seek out confirming information and rely on these tools. This could diminish their critical thinking and decision-making abilities [35, 42].Accessibility Bias : Students with disabilities and those from low-income backgrounds often have limited access to technology and resources [43, 44]. In an AI-driven educational landscape, these marginalized groups face a disadvantage because they often lack the necessary resources and technical infrastructure to thrive. This creates imbalances in educational opportunities [22, 45].Cognitive Biases : Cognitive bias denotes systematic deviations from rational thinking or established standards in judgment, which typically arise as the mind seeks to simplify the processing of information. Within the context of AI systems, these biases can serve to reinforce societal inequities, perpetuate stereotypes, and undermine the integrity of decision-making processes [46]. Several factors contribute to the emergence of cognitive biases, such as mental shortcuts or heuristics that simplify decision-making, emotions, social influences, and cultural norms [33]. Pum [33] highlights several cognitive biases particularly relevant to the development of AI, namely confirmation bias, anchoring bias, availability bias, and groupthink. Confirmation bias is the tendency to look for or interpret information that supports one’s existing beliefs. The anchoring bias is the inclination to rely excessively on the initial piece of information when making decisions [33, 47, 48]. The availability heuristic is the tendency to judge the likelihood of an occasion based on how readily examples of it come to mind rather than on statistical evidence. Finally, groupthink is the tendency of groups to prioritize consensus and harmony over critically evaluating alternatives, often resulting in poor or biased decision-making [33].Transparency Bias : Transparency is essential for the public to accept AI technology. Therefore, a lack of transparency can lead to distrust and hinder public acceptance. Similarly, a deficiency of transparency in AI systems can prevent teachers, students, and administrators from understanding how decisions are made, which can result in a deficit of accountability and trust [42]. In addition, the complexities of deep learning algorithms can hide accountability, creating ethically problematic situations where AI decisions are unclear, which increases the difficulties related to fairness and justice in systems [49] used in K-12 schools.
The data used to teach the AI may not be diverse or balanced enough. Flaws in the algorithmic design or unconscious human biases during the AI’s training can inadvertently introduce prejudice. In this context, Zhang and Zhang [50] emphasize the importance of incorporating ethical algorithms into AI systems to ensure reliable, secure, and fair decision-making processes. Further exploring the ethical dimensions of AI, research [51, 52, 53, 54] on AI ethics has identified three areas – the characteristics of AI, the human factor, and the social impact of AI – that could raise ethical concerns regarding the use of AI. Each category emphasizes a distinct, yet interconnected aspect of AI’s role in society. The goal is to develop a holistic understanding of how AI technologies intertwine with ethical considerations.
3.2 Ethical challenges resulted from the features of AI
The first category (features of AI) focuses on the inherent characteristics of AI systems that give rise to ethical dilemmas [55]. Following a thorough review of the literature, Siau and Wang [54] identified the following ethical challenges resulting from AI features: transparency, data security and privacy, autonomy, intentionality, and responsibility.
In addition, data collected from the internet carries risks, particularly surrounding privacy and safety concerns [57, 58]. Accordingly, one ethical concern related to students revolves around
In terms of
3.3 Human factors and social impacts
As mentioned above, ethical issues may arise from human factors when preparing, collecting, annotating, or interpreting data. For example, sample treatment bias occurs when test sets are not representative of the entire population. For example, test sets that target specific viewer demographics are not representative of the entire population [39]. So, human factors highlight the components contributing to ethical risks in the implementation of AI. Notably, AI applications are trained using datasets created by humans; therefore, these tools may unintentionally perpetuate existing human biases. In this context, Kargl et al. [62] emphasize that human biases in training data and decision-making processes can inadvertently impact AI systems, leading to outcomes that do not align with established ethical standards. Hence, transparency and accountability become essential elements in addressing the ethical considerations associated with AI systems, particularly those pertaining to mitigating bias [63, 64]. This raises critical questions about who is responsible when an AI system makes a mistake or causes harm, thereby highlighting the issues of accountability and responsibility.
Besides, due to limitations in perspective or recall, people may unintentionally evaluate AI products in a biased manner. Biases, such as confirmation bias, can distort evaluators’ perceptions, leading to inaccurate performance assessments. Using multiple evaluators and standardizing performance evaluation metrics enhances the reliability of assessments and reduces the impact of individual biases [39].
External factors can inadvertently affect the inputs and outputs of AI systems, distorting the relationships between variables. This can lead to misinterpretations that falsely correlate unrelated variables. Besides, different annotators may offer subjective interpretations influenced by their cultural backgrounds or personal biases. These interpretations can lead to significant inconsistencies in the training data. Thus, label bias arises from these inconsistencies [39, 65]. Therefore, the third category (social impact) emphasizes that integrating AI technologies into society requires addressing more than just technical challenges. It also reflects broader social dynamics and values. Huang [66] argues that the ethical implications of AI could have a profound impact on societal structure, particularly concerning critical areas such as privacy, employment, and human rights. These concerns are addressed below.
3.4 Considerations of justice, equality, privacy, and the empowerment of education
When discussing the ethical implications of AI in education, we are essentially exploring the possibility that these technologies could cause harm or produce undesirable results. Hence, the central question is whether AI systems can consistently produce accurate and dependable results. For example, if an AI system utilized for assessment provides students with inaccurate feedback, or if a personalized learning path proves ineffective due to the AI misjudging a student’s needs, it can directly hinder their learning. Therefore, the goal becomes ensuring that AI systems consistently yield precise and trustworthy outcomes. In this context, initially, accuracy and reliability emerge as critical issues.
Other ethical considerations relate to the quality and reliability of AI-generated content. As previously mentioned, algorithms that make decisions based on datasets can obscure important details regarding student performance. Such obscurities may lead to misunderstandings and incorrect conclusions [67, 68]. This concern raises questions about the consistency and reliability of AI systems. Reliance on AI for evaluation and feedback can disproportionately influence the behaviors of both educators and students. The biases and ethical challenges associated with AI hold the potential to undermine teachers’ authority in the educational process. In some instances, students may receive inaccurate feedback that can impair their learning experience. Therefore, when relying on AI, it is necessary to rigorously evaluate its implications for students and educators, as underscored by Yu and Yu [69]. The overarching goal is to ensure that output from AI systems remains precise and reliable. Similarly, utilizing AI for formative assessments raises significant ethical concerns.
Second, students from marginalized demographics often lack the resources necessary to effectively use these technologies. However, it is crucial to make AI resources accessible while avoiding the potential of exacerbating existing inequalities in educational systems. Therefore, prioritizing equity is essential when integrating AI systems into schools [22, 24, 43, 45]. Besides, using AI tools designed for assessments or personalized learning could unintentionally disadvantage specific groups of students, especially those from marginalized communities [6, 23, 24, 43]. Without careful design and monitoring, AI systems can perpetuate or amplify existing social biases. This results in discrimination and bias inherent in AI algorithms that can negatively impact students, particularly those from disadvantaged and marginalized groups. Such algorithms may further exacerbate existing societal injustices, including racism, sexism, and xenophobia. For example, as scholars [52, 53, 54] highlight, an AI system could treat students differently based on their background, gender, or other characteristics. This might result in unfair grading, unequal access to resources, or inappropriate learning recommendations. Studies [24, 70, 71, 72] have documented cases where AI tools exhibit biased outcomes, negatively affecting marginalized student groups. Hence, if developed using biased data, AI systems could perpetuate existing inequalities within the educational framework. This exacerbates existing educational inequities.
Addressing these challenges necessitates a collaborative approach involving educators, policymakers, and technologists to design inclusive systems [59, 73].
Another ethical dilemma involves the substantial amount of data processing required by AI systems. This raises concerns (e.g., [22, 66]) regarding students’ rights, the ethical management of their data, and the risks of data privacy and surveillance. AI tools frequently collect sensitive student information, such as grades, learning styles, and behavioral patterns. For example, Alali and Wardat [43] state that many AI systems require the collection of extensive data, which could lead to privacy invasions if not managed appropriately. Such situations prompt a wealth of ethical questions, particularly regarding whether this information could be accessed or misused without authorization.
Privacy and intimacy in learning embody a relational dimension that is significantly influenced by the extensive data collection practices of AI systems in education. Even with anonymization, the pervasive nature of such data collection can foster a sense of constant surveillance among students and teachers. Consequently, this environment can inhibit their comfort in self-expression, questioning, and engaging in vulnerable learning scenarios. Ethical concerns arise regarding how this widespread data collection can undermine the essential sense of privacy and psychological safety that is critical in fostering effective learning relationships. Interactions that should feel intimate may instead become characterized by a sense of being monitored.
To address the pervasive ethical concerns, it becomes vital to establish robust regulatory frameworks and ongoing support mechanisms for the responsible use of AI within K-12 environments. Continuous assessment and refinement of these ethical frameworks are imperative for tackling the challenges posed by rapid technological advancements. Therefore, schools must proactively establish ethical guidelines for collecting, storing, and using data to ensure a safe and supportive learning environment for all stakeholders. One pertinent example is the Family Educational Rights and Privacy Act (FERPA) in the United States [74], which underscores the need to protect student privacy in schools.
AI tools possess the potential to significantly alter the dynamic between teachers and students, as they may lead educators to rely more on technology for instruction rather than fostering direct interactions [32, 75]. This shift has the potential to transform relationships among students, educators, and AI systems; however, it carries the risk of diminishing essential human interactions that are vital for emotional and social learning [76]. AI can significantly impact students’ emotional and social development. A supportive learning environment is essential for nurturing empathy, negotiation skills, conflict resolution practices, and the ability to interpret social cues. However, if AI mediates too many interactions or provides feedback based exclusively on algorithms, students may have fewer opportunities to practice and refine these skills. Therefore, AI must be designed to complement and support the development of these vital social skills rather than replace them. While AI tools present benefits such as personalized learning and automated feedback, concerns have been raised regarding the potential for these technologies to depersonalize the learning experience, as previously indicated. An overreliance on AI could diminish the depth of human connections, empathy, and personalized mentorship that teachers traditionally provide, all of which are essential for fostering a nurturing educational environment. Students and educators may develop trust in, or even become dependent on, AI systems for information, decision-making, or emotional support. However, this reliance could weaken if the AI produces inaccurate results.
AI systems’ influence extends beyond individual learning experience; it significantly impacts peer interaction and collaboration within the classroom. While educational AI platforms customize learning by tailoring content to individual students, they may unintentionally limit opportunities for collaborative learning, group problem solving, and the cultivation of social skills among peers. This brings up a crucial question: In what ways can AI be designed to encourage meaningful peer interactions, promote diverse perspectives, and support the acquisition of essential social skills, for example, teamwork and communication? As AI systems take over more administrative responsibilities, educators (e.g., [77, 78]) express concerns about potentially being relegated to the roles of mere facilitators or overseers of technology, rather than active participants in the teaching and learning process. This transformation could erode traditional pedagogical approaches that recognize teachers’ unique ability to foster intellectual curiosity and critical thinking in their students [79]. Finally, the increased use of AI in education by students raises significant issues pertaining to academic integrity. AI tools, such as homework writing assistants and chatbots, pose a risk of enabling academic dishonesty by circumventing traditional learning methods [80, 81]. As scholars [75] have emphasized, this exacerbates issues like plagiarism, thereby creating additional challenges within the educational landscape.
Research highlights the risks associated with AI in an educational context. For example, Mambile and Mwogosi [82] emphasize the importance of maintaining a complementary relationship between AI applications and traditional teaching methods. They argue that AI should enhance, rather than replace, direct interactions between educators and students. Similarly, Zhou et al. [83] point out that the advent of AI technologies is causing a profound transformation in education by reshaping the learning processes for various stakeholders. Yim et al. [84] further suggest that overreliance on AI could undermine vital interpersonal skills developed through direct teacher-student interactions. Moreover, Çela et al. [85] assert that an AI-centric teaching approach may diminish critical cognitive abilities, resulting in both educators and students becoming less proficient in these areas.
4. Principles for effectively addressing and mitigating ethical challenges
Scholars such as Ferrara [86] highlight that incorporating a wide range of socioeconomic backgrounds, ethnicities, and academic performance levels into these datasets mitigates the risk of systemic bias in AI outputs. Moreover, the ethical considerations surrounding the use of AI in basic education impact both individuals and society. As scholars (e.g., [22, 30]) emphasize, overcoming these challenges requires a comprehensive, proactive approach that prioritizes transparency, justice, accountability, and student participation.
Through a systematic literature review, Ashok et al. [87] identified an ethical framework with several key principles to guide the responsible development and deployment of AI. These principles are transparency, non-discrimination and fairness, data protection and privacy, accountability, safety and security, inclusiveness, and sustainability. Besides, Adams et al. [88] proposed a set of ethical principles designed to ensure that AI technologies improve educational outcomes. These are equity, transparency, accountability, privacy, and collaboration. Similarly, Webb et al. [89] proposed a set of comprehensive ethical principles to guide the ethical governance of AI technologies in K-12 education for practitioners and policymakers. These are equity and inclusivity, transparency and explainability, privacy and data protection, responsibility and accountability, collaboration among stakeholders, and continuous evaluation and adaptation.
Transparency is the principle of clearly and openly communicating how AI algorithms work. It also includes what data they use and the decisions they make [89]. Transparency makes AI systems understandable and explainable to stakeholders, which promotes trust and accountability [87]. Transparency is essential for fostering trust among educators, students, and stakeholders. According to this principle, the mechanisms through which AI systems process information and manage learning outcomes should be made public. This allows stakeholders to critically evaluate the impact of AI tools in educational settings [88]. Besides, explainability is a critical subset of this principle, stipulating that teachers, students, and parents should understand how AI technologies generate recommendations or decisions [89].
Conversely, accountability refers to the responsibility of educators, technology developers, and policymakers when deploying AI systems. To be responsible and accountable, clear lines of responsibility and accountability must be established to ensure that stakeholders can be held responsible for the impacts of AI on students [88, 89]. According to Adams et al. [88], this principle promotes governance structures that prioritize ethical decision-making and responsiveness to the potential adverse effects of using AI in education. Klimova et al. [90] argue that monitoring and addressing the potential adverse effects of AI algorithms is essential to the ethical use of AI in education. Therefore, transparent AI is therefore necessary so that educators and stakeholders can explain to students and parents how these systems make decisions. Consequently, as scholars (e.g., [91]) have pointed out, transparency ensures accountability for decisions generated by AI systems and clarifies how AI algorithms are used in schools.
The principle of equity emphasizes providing accessible AI-enabled educational opportunities to every student, no matter their socioeconomic status, race, or geographic region. This principle emphasizes addressing biases that may arise in AI algorithms and ensuring equitable distribution of technological resources to promote an inclusive educational environment [88]. Therefore, principles of equity and inclusiveness stress the necessity of designing equitable and inclusive AI systems. A quality education is guaranteed for all students, irrespective of their background or abilities [89]. Hence, AI must be developed in a way that avoids bias and ensures the equitable treatment of different user groups [87]. Besides, AI has the potential to counteract existing inequalities by offering personalized learning experiences that address different needs [89].
The principle of privacy advocates for the protection of student data, as AI systems often necessitate extensive data collection, causing concerns with regard to data security and potential misuse. Data privacy obligations include protecting student privacy and complying with relevant data protection legislation. Data privacy requires that any data collection be justifiable and minimal. Furthermore, data collection procedures must be carried out with the full knowledge and consent of students and parents. These measures safeguard individual privacy rights [88, 89].
Effective integration of AI into educational activities requires the collaborative efforts of all stakeholders, including students, educators, parents, and AI developers. Collaboration emphasizes the importance of partnerships among educators, technologists, parents, and policymakers when integrating AI tools into educational contexts. This principle encourages participatory approaches in the design and implementation of AI systems, where the insights and experiences of those most affected by these technologies inform their development [88, 89].
Finally, it is crucial to implement accountability measures for individuals or organizations when errors or unforeseen consequences arise from the use of AI systems. This practice helps prevent unethical conduct. It also ensures that those responsible for any harm caused by AI technologies are held accountable.
Thus, accountability is also crucial for the responsible use of AI [41]. Figure 2 shows the main ethical principles that can be used to address ethical challenges.
Figure 2.
The main ethical principles.
As more schools adopt AI-driven tools and systems, concerns regarding bias, data protection, accountability, and transparency are growing. These concerns demonstrate that these are global challenges requiring a collective response. In response to these concerns, governments and organizations such as UNESCO have proposed or enacted various legislative measures.
In 2021, the European Union [92] introduced the AI Act. This extensive framework classifies AI systems according to their potential threats to human rights and public safety. The Act aims to promote the ethical advancement and application of AI technology by creating a risk-based classification system. The act also promotes innovation and supports AI startups in Europe. It includes a timeline for implementation and compliance. In China, the Ministry of Education has mandated AI literacy as part of the K-12 curriculum, reflecting an acknowledgment of AI’s essential role in the economy and society [93]. The Turkish Ministry of National Education [94] published the guide “Artificial Intelligence and Education: Applied Prompt Engineering and Innovative Learning Strategies with Generative Tools for Teachers” to help K-12 teachers effectively use AI tools in the classroom by 2025.
UNESCO has also made significant efforts in this regard. In this context, it has published several reports addressing the ethics of AI in K-12 schools. For example, it shared “AI and education Guidance for policy-makers” [95], “Recommendation on the ethics of artificial intelligence” [96], and “K-12 AI curricula: A mapping of government-endorsed AI curricula” [97]. The report, “K-12 AI Curricula: A Mapping of Government-Endorsed AI Curricula” emphasizes the importance of including AI ethics as a core topic in K-12 AI education. The report identifies seven categories of AI ethics: access, ethical terms, definitions, and examples, bias, privacy and security, intellectual property, human agency, and transparency and explainability [96]. Table 1 shows an explanation of these categories as explained in the report ([96], p. 41).
| Sub-domain | Learning outcomes |
|---|---|
| Ethical terms |
|
| Access |
|
| Bias |
|
| Intellectual property |
|
| Privacy and security |
|
| Transperancy |
|
| Human agency |
|
Table 1.
To address ethical considerations, scholars [88, 89] suggested that schools and technology developers first adopt ethical design practices that integrate these principles. Schools should collaborate with technologists to develop ethical guidelines for developing and deploying AI technologies. These guidelines should be based on principles such as equity, transparency, accountability, privacy, and collaboration. Besides, according to international policy guidance, the equitable and inclusive use of AI in education should be encouraged, and educational data should be safeguarded to ensure its ethical, transparent, and auditable use [6].
In this context, initially, a multitude of viewpoints must be integrated into the design process, and systems must be constructed with inclusivity and equity in schools [89]. To ensure equality, diversity, and inclusion in designing AI services, measurable targets must be established and monitored. To allow equitable access to educational AI benefits, the appropriate infrastructure, including internet access, software, and hardware, must be strengthened. To reach the most vulnerable groups in society, measures must be implemented. AI programs with a proven track record of inclusivity should be prioritized [95]. According to Khan et al. [91], involving the school community in discussions about AI technology ensures that different viewpoints are considered when developing educational AI policies. Therefore, the integration of AI technology should prioritize fostering inclusivity within educational ecosystems. Besides, when collecting and selecting training data, educators should ensure that underrepresented and diverse populations in schools are not excluded.
Second, schools should establish transparent communication protocols for implementing AI technologies. They should also provide educators and students with the necessary training and resources to understand these tools [89]. As Zhang et al. [98] argue, ethical governance is paramount as AI technologies are incorporated into educational settings. This includes establishing clear guidelines for transparency, fairness, and accountability in AI systems used by educators. Therefore, two core aspects of integrating AI into K-12 education are ethical principles and governance structures that promote equity and inclusion. In addition, ensuring that all students have equal access to educational opportunities and resources is essential [99].
Third, schools must implement robust data protection policies that respect student privacy and ensure compliance with international standards for data usage [89]. To ensure security and data privacy, schools must first acknowledge their legal obligation to protect data regarding students and actively comply with relevant relislation, for example, the Family Educational Rights and Privacy Act (FERPA) and the Children’s Online Privacy Protection Act (COPPA). These regulations establish strict requirements for collecting and handling student data [22, 100]. Furthermore, schools should provide clear guidelines on how student data is collected, stored, protected, and used in school operations.
Fourth, clear accountability structures should be established within educational systems to ensure responsible governance of AI technologies. This may involve creating oversight committees that include representation from diverse stakeholders. Fifth, teachers should encourage active collaboration among students, parents, and local communities throughout the AI application process. This engagement should be viewed as an opportunity for shared learning and ethical deliberation. Lastly, schools should prioritize researching the implications of AI technologies and commit to ongoing evaluation processes. Based on stakeholder feedback and research, schools should adjust their practices. In line with this framework educators must understand the complexities introduced by AI systems, which include recognizing the biases inherent in technology [101]. Accordingly, it is equally important to educate educators and administrators about the ethical implications of using AI to mitigate bias. Importantly, Jiang and Pardos [102] demonstrate that formative assessment tools powered by AI are crucial for identifying unintended biases. Therefore, continuously monitoring and evaluating AI systems plays a pivotal role in maintaining fairness in educational practices. Moreover, effective bias mitigation begins with transparent practices when developing and deploying AI algorithms. Smyrnaiou et al. [103] advocate for ethical practices to prevent AI from discriminating against certain student demographics. One way to achieve this is by diversifying the training data to include the voices and experiences of students, thereby ensuring equitable algorithmic decisions [86]. Finally, as Vorisek et al. [104] recommend, continuously monitoring AI systems after deployment is crucial for evaluating their effectiveness in addressing issues of diversity, equity, and inclusion.
In addition, autonomy refers to individuals’ capacity to make their own choices and govern themselves. Concurrently, the agency emphasizes individuals’ ability to act independently and influence the course of their lives, especially in educational settings [105]. Thus, autonomy and agency shape the way students, teachers, and AI technologies interact. These principles establish a framework that promotes ethical educational practices. Consequently, as Rarasati and Pramana [106] underscores, to create effective learning environments with AI tools, teachers need professional autonomy. Therefore, they must be able to make informed decisions about their teaching practices, which necessitates professional development and continuous training in AI literacy and its ethical implications.
Research (e.g., [107]) indicates that students who are educated in the foundational principles and applications of AI exhibit increased confidence when using these technologies. Therefore, ensuring that students clearly understand the available AI tools is the first step in promoting autonomy and agency. Besides, Zhao et al. [108] suggest that many students are concerned about how AI might affect their privacy and autonomy. They further recommend that giving students more control over their learning could encourage them to embrace AI in their education. Within this context, it is crucial to recognize that AI could unintentionally limit students’ freedom and ability to act independently. Thus, fostering a culture of informed decision-making is essential.
Facilitating projects where students utilize AI tools to address complex societal issues can encourage critical thinking and ethical reasoning. In this context, teachers can present cases that illustrate both the positive and negative impacts of AI on decision-making. This approach enables students to critically evaluate the outcomes of AI applications. Besides, educators can implement scenario-based learning, which involves presenting students with realistic dilemmas involving AI decision-making. Furthermore, engaging students can be enhanced through the incorporation of interactive tools, such as ethical dilemma discussions, thereby supporting a dialogical approach and fostering increased participation [109]. According to Alali and Wardat [43], integrating ethical discussions about AI usage into the curriculum prepares students to navigate the complexities of modern technology responsibly. Therefore, teachers can facilitate conversations that invite students to express their perspectives on AI’s role in their lives and the broader implications it entails.
To effectively integrate AI technologies into education and address related ethical challenges, it is essential for educators to be equipped with the necessary skills and knowledge [91]. Therefore, teacher development programs should prioritize improving teachers’ AI literacy and educating them about the potential risks and ethical considerations of using AI in K-12 schools. Educators and students should have a solid understanding of how AI systems operate. Therefore, integrating AI into K-12 schools must be grounded in ethical standards that emphasize clear guidelines.
It is crucial that the methods by which AI operates within educational settings are inherently just and equitable. For instance, if an AI system is employed for admissions or resource allocation, the algorithms and criteria used must be fair, transparent, and consistently applied to all students, regardless of their outcomes. In this respect, developing appropriate pedagogical strategies is essential, as these strategies should leverage AI technologies while carefully considering ethical implications. Nevertheless, Ng et al. [56] demonstrated that gamified learning and inquiry-based projects can stimulate student interest and foster meaningful discussions about the ethical applications of AI in society. To promote ethical awareness, schools must revise their curricula to prepare students for the ethical challenges that will inevitably arise from their future interactions with AI [73].
In conclusion, technology continues to transform the educational landscape worldwide. Educators and policymakers must adopt a comparative perspective. They must also embrace a comprehensive ethical approach. This framework should prioritize accountability, transparency, and inclusivity to improve the AI-based educational experience for all students.
Acknowledgments
There is no funding for this chapter.
Conflict of interest
The author declares no conflict of interest.
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