While Artificial Intelligence in Education (AIED) research has at its core the desire to support student learning, experience from other AI domains suggest that such ethical intentions are not by themselves sufficient. There is also the need to consider explicitly issues such as fairness, accountability, transparency, bias, autonomy, agency, and inclusion. At a more general level, there is also a need to differentiate between doing ethical things and doing things ethically, to understand and to make pedagogical choices that are ethical, and to account for the ever-present possibility of unintended consequences. However, addressing these and related questions is far from trivial. As a first step towards addressing this critical gap, we invited 60 of the AIED community’s leading researchers to respond to a survey of questions about ethics and the application of AI in educational contexts. In this paper, we first introduce issues around the ethics of AI in education. Next, we summarise the contributions of the 17 respondents, and discuss the complex issues that they raised. Specific outcomes include the recognition that most AIED researchers are not trained to tackle the emerging ethical questions. A well-designed framework for engaging with ethics of AIED that combined a multidisciplinary approach and a set of robust guidelines seems vital in this context.

Evidence from various domains underlines the critical role that human factors, and especially trust, play in adopting technology by practitioners. In the case of Artificial Intelligence (AI) powered tools, the issue is even more complex due to practitioners' AI‐specific misconceptions, myths and fears (e.g., mass unemployment and privacy violations). In recent years, AI has been incorporated increasingly into K‐12 education. However, little research has been conducted on the trust and attitudes of K‐12 teachers towards the use and adoption of AI‐powered Educational Technology (AI‐EdTech). This paper sheds light on teachers' trust in AI‐EdTech and presents effective professional development strategies to increase teachers' trust and willingness to apply AI‐EdTech in their classrooms. Our experiments with K‐12 science teachers were conducted around their interactions with a specific AI‐powered assessment tool (termed AI‐Grader) using both synthetic and real data. The results indicate that presenting teachers with some explanations of (i) how AI makes decisions, particularly compared to the human experts, and (ii) how AI can complement and give additional strengths to teachers, rather than replacing them, can reduce teachers' concerns and improve their trust in AI‐EdTech. The contribution of this research is threefold. First, it emphasizes the importance of increasing teachers' theoretical and practical knowledge about AI in educational settings to gain their trust in AI‐EdTech in K‐12 education. Second, it presents a teacher professional development program (PDP), as well as the discourse analysis of teachers who completed it. Third, based on the results observed, it presents clear suggestions for future PDPs aiming to improve teachers' trust in AI‐EdTech. Practitioner notes: What is already known about this topic Human factors, and especially trust, play a critical role in practitioners' adoption of technology. In recent years, AI has been incorporated increasingly into K‐12 education. Little research has been conducted on the trust and attitudes of K‐12 teachers towards the use and adoption of AI‐powered Educational Technology. What this paper adds This research emphasizes the importance of increasing teachers' theoretical and practical knowledge about AI in educational settings to gain their trust in AI‐EdTech in K‐12 education. It presents a teacher professional development program (PDP) to increase teachers' trust in AI‐EdTech, as well as the discourse analysis of teachers who completed it. It presents clear suggestions for future PDPs aiming at improving teachers' trust in AI‐EdTech. Implications for practice and/or policy Pre‐ and in‐service teacher education programs that aim to increase teachers' trust in AI‐EdTech should include a section providing teachers with a basic understanding of AI. PDPs aimed to increase teachers' trust in AI‐EdTech should focus on concrete pedagogical tasks and specific AI‐powered tools that are considered by teachers as helpful and worth the effort to learn. AI‐EdTech should not restrict teachers to follow specific pedagogical scenarios, but rather provide teachers with the freedom to design and implement various types of pedagogies that meet their preferences, students' needs, and classroom reality. Teacher agency is key to gaining their trust. AI‐EdTech should allow teachers to review, modify, and if necessary, override AI‐based recommendations before they are sent to students.

This paper presents a multidimensional view of AI's role in education, emphasising the intricate interplay among AI, analytics and human learning processes. Here, I challenge the prevalent narrow conceptualisation of AI as tools in Education, exemplified in generative AI tools, and argue for the importance of alternative conceptualisations of AI for achieving human–AI hybrid intelligence. I highlight the differences between human intelligence and artificial information processing, the importance of hybrid human–AI systems to extend human cognition and posit that AI can also serve as an instrument for understanding human learning. Early learning sciences and AI in Education Research (AIED), which saw AI as an analogy for human intelligence, have diverged from this perspective, prompting a need to rekindle this connection. The paper presents three unique conceptualisations of AI: the externalisation of human cognition, the internalisation of AI models to influence human mental models and the extension of human cognition via tightly coupled human–AI hybrid intelligence systems. Examples from current research and practice are examined as instances of the three conceptualisations in education, highlighting the potential value and limitations of each conceptualisation for human competence development, as well as the perils of overemphasis on approaches that replace human learning opportunities with AI tools. The paper concludes with advocacy for a broader approach to AIED that goes beyond considerations on the design and development of AI and includes educating people about AI and innovating educational systems to remain relevant in an AI ubiquitous world.

Artificial Intelligence (AI) in Education claims to have the potential for building personalised curricula, as well as bringing opportunities for democratising education and creating a renaissance of new ways of teaching and learning. Millions of students are starting to benefit from the use of these technologies, but millions more around the world are not, due to the digital divide and deep pre-existing social and educational inequalities. If this trend continues, the first large-scale delivery of AI in Education could lead to greater educational inequality, along with a global misallocation of educational resources motivated by the current techno-solutionist narrative, which proposes technological solutions as a quick and flawless way to solve complex real-world problems. This work focuses on posing questions about the future of AI in Education, intending to initiate the pressing conversation that could set the right foundations (e.g., inclusion and diversity) for a new generation of education that is permeated with AI technology. The main goal of our opinion piece is to conceptualise a sustainable, large-scale and inclusive AI for the education ecosystem that facilitates equitable, high-quality lifelong learning opportunities for all. The contribution starts by synthesising how AI might change how we learn and teach, focusing on the case of personalised learning companions and assistive technology for disability. Then, we move on to discuss some socio-technical features that will be crucial to avoiding the perils of these AI systems worldwide (and perhaps ensuring their success by leveraging more inclusive education). This work also discusses the potential of using AI together with free, participatory and democratic resources, such as Wikipedia, Open Educational Resources and open-source tools. We emphasise the need for collectively designing human-centred, transparent, interactive and collaborative AI-based algorithms that empower and give complete agency to stakeholders, as well as supporting new emerging pedagogies. Finally, we ask what it would take for this educational revolution to provide egalitarian and empowering access to education that transcends any political, cultural, language, geographical and learning-ability barriers, so that educational systems can be responsive to all learners’ needs.