As Artificial Intelligence (AI) becomes increasingly integrated into various facets of our lives, including the educational domain, it is important to apply ethical principles to guide the development and deployment of AI systems. This ethically guided approach aims to mitigate potential harms or discriminatory outcomes resulting from AI algorithms. As a result, various ethical regulations and guidelines for AI ethics have emerged at the corporate, national, and supranational levels. However, the literature has paid relatively scant attention to the specific ethical considerations within the domain of AI in Education (AIED). AIED ethics represents a complex intersection, necessitating the combination of general AI ethics and the ethics of educational technology. This research aims to find the key constituents of an ethical framework for educational stakeholders of AIED that can be used to identify ethical issues in an AIED system. In this paper, we outline the methodology employed in this research to create an ethical framework for AIED. A systematic literature review will first look into the ethics of AI, the ethics of education and the ethics of AIED to consolidate the Ethical Values (EVs) and Ethical norms (ENs) for AIED ethics. Building on this knowledge from the literature, additional ENs will be collected through stakeholder consultation. These ENs will then be ranked by experts and used to form an ethical framework.
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Artificial Intelligence (AI) is reshaping the world in
profound ways and has a widespread impact on our lives,
including education. The usage of AI in classrooms and
in education is promising and provides opportunities
to improve the education process. AI has been applied
in educational contexts in a range of contexts varying
from automation of administrative processes and tasks to
curriculum and content development, instruction to
understanding and improving students’ learning processes
through analysis of student data [
Over the past decade, the use of AI tools to support
and enhance learning has grown exponentially [
An ethical guidance for AIED is necessary to reduce the negative impacts caused due to propagation of historical biases and discrimination that can result from the usage of AIED. At the same time, it is important to protect the privacy and autonomy of students and teachers so that the data collected by educational institutes cannot be used for other purposes. Hence, there is a need to use an ethical framework to regulate AIED.
In addition to considering general AI ethics, AIED ethics has to also consider the ethics of education. The overlap between the ethics of AI, ethics of education and ethics of AIED suggests that they should draw inspiration from each other [5]. The usage of AI technologies in education raises questions linked to ethical issues such as data ownership and control, privacy, biases in algorithms, data management, transparency, and a need for educational context [5]. Despite these concerns raised by AIED systems, limited attention has been paid to the row from both domains and add additional ethical values as required by the domain specifically, while also considering the applicability of these values to the domain of AIED [5]. Due to these complexities, AIED ethics deserves attention.
The main research question guiding this research is: to identify ethical issues in an AIED system?”. This can be divided into three sub-questions for the three groups of educational stakeholders in AIED - students, educators and educational institutes. Thus, the three sub research questions are: increase in AIED literature over the years; (c) AIED re- ethics of AIED [6, 5, 7, 8, 9, 10]. AIED ethics could borwell as the right not to be discriminated against and per- “What are the key constituents of an ethical framework petuate historical patterns of discrimination” [4, p. 26]. for educational stakeholders of AIED that should be used © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License
framework for educational institutes of AIED that should be used to identify ethical issues in an AIED system? 2. RQ2: What are the key constituents of an ethical framework for educators of AIED that should be used to identify ethical issues in an AIED system? 3. RQ3: What are the key constituents of an ethical framework for students of AIED that should be used to identify ethical issues in an AIED system?
framework for AIED, this research follows an adapted version of the generalizable model for developing Codes of Practice (CoP) developed by Sclater. The ‘CoP for Learning Analytics’ developed by the authors contains a set of guidelines and some ethical principles [11]. Due to the similarity of the format of the developed CoP to an ethical framework and the systematic nature of the generalizable methodology, we chose to use this approach to develop our ethical framework for AIED.
The author presents the activities followed as a basis Figure 1: Research methodology for a generalizable model that can be used for developing CoP in other professions or areas of education [11]. The generalizable model involves developing five products: for AIED. Following this, the gaps in literature will be 1) a literature review identifying ethical, legal, and logis- filled in through stakeholder consultation. The output tical concerns, 2) a taxonomy of issues refined through of the stakeholder consultation will then be ranked by expert consultation, 3) a draft CoP, 4) a final publicly experts and used to form an ethical framework. The released CoP incorporating feedback from public consul- stakeholder consultation and expert consultation will tation, and 5) a supporting website with guidance and ensure that the viewpoints of all the stakeholders of AIED case studies [11]. An advisory group of experts and stake- can be incorporated into the framework. holders provides input throughout. As the CoP is piloted, feedback informs updates to subsequent versions. 2.1. Systematic literature review
This research uses the generalizable model developed by Sclater but makes three modifications. Firstly, we start The existing literature on AIED ethics is lacking in three with consulting stakeholders and incorporating their in- aspects: (a) a theoretical definition of the essence of AIED put from the beginning. This is because the ethics of ethics, (b) a hierarchical classification of AIED ethics, AIED is a relatively new domain and has a direct impact and (c) reflection on the regulations [ 12]. As the first on the lives of the stakeholders. Secondly, the experts step of this research, a systematic literature review (SLR) are involved in a later stage of the model to review the was conducted with an aim to address these gaps by input collected from the stakeholders. Additionaly, we identifying and defining the key constituents of AIED gather input about the design of the framework from the ethics. experts. Lastly, due to time limitations, we do not follow In this SLR, the normative approach to ethics - which the last two steps described in the generalizable model. proposes “how to act, how to live and what kind of person Figure 1 shows adapted the methodology followed in this to be” [13, p. 2] - was followed. Ethics is defined in terms research. of values and norms. Values are abstract ideas that are
To answer the main research question, a systematic strived for via certain types of behavior while norms are literature review (SLR) that looks into the ethics of AI, rules that specify actions to achieve certain values [14]. the ethics of education and the ethics of AIED will first Using this definition of ethics, the key constituents of an be conducted. This SLR will serve as the foundation by ethical framework are identified as ethical values (EVs) identifying the key constituents for an ethical framework and ethical norms (ENs). The SLR was guided by two central research questions:
2. What are the ethical norms (ENs) for AIED? work for AIED. This SLR distinguished the key constituents that would comprise such an ethical framework, thereby setting the stage for further elaboration and fleshing out of these key constituents.
This SLR aims to identify the main EVs and ENs for AIED by analyzing relevant literature published between 2.2. Stakeholder consultation 2010 and 2022 that is available in the English language.
Through a comprehensive database search and back- This research study aims to explore the ethical perspecward snowballing technique, a total of 25 articles were tives of key stakeholders in the field of AIED. The SLR identified and included for analysis. The Preferred Re- highlighted a lack of ENs for end users and educational porting Items for Systematic reviews and Meta-Analyses institutes. Building on this result, the target population (PRISMA) method [15] was used to ensure transparency for the stakeholder consultation comprised three distinct and reproducibility of the SLR. roups: students, teachers, and educational administrators
To identify the EVs, the definitions found within the who act as representatives from educational institutions. literature were collected and reported. To analyse the To facilitate an in-depth exploration of AIED ethics, the EVs, they were grouped into common themes or topics. study employs a qualitative research methodology involvThe grouping criterion was based on the identification of ing focus group discussions. For this study, participants common key terms that appear consistently across mul- were recruited based on the match of their profile with the tiple definitions of the EVs. Subsequently, the emergent stakeholder groups identified, i.e. educational administrathemes were assigned descriptive labels that encapsu- tors, students and educators active in higher education. lated the overarching meaning and conceptual essence The participants were approached during workshops, conveyed by the set of definitions within each respec- presentations at conferences, summer schools, and other tive cluster. This thematic analysis process allowed for academic events related to artificial intelligence, educaa systematic consolidation and organization of the EVs tion, or both fields. After initially showing interest in identified in the literature. The analysis of the EVs re- participating, potential participants received an email invealed six main EVs of AIED: non-discrimination, data vitation along with a letter providing information about stewardship, human oversight, goodwill, explicability, the details of the study. This study was performed in the and educational aptness. Netherlands, and all participants were active in higher
ENs were identified by looking for keywords such as education in said country. ‘norms’, ‘guidelines’, ‘regulations’, ‘recommendations’ or Five separate focus groups were conducted in the peanother synonym of these terms in the selected articles. riod from March 2024 to May 2024 (inclusive). Two focus Four main stakeholder groups were identified from the groups each were conducted for students and teachers, literature: end users, developers, regulators and educa- and one was conducted for educational administrators. tional institutes [16]. The ENs identified in the literature Each focus group was conducted with the group size were categorized according to the stakeholder groups of five to eight participants to optimize participant enthey are relevant for and the corresponding main EV gagement and interaction [17]. The discussions revolved they uphold. Subsequently, these two categorizations around three dilemmas and ethical considerations surwere combined into a matrix, mapping ENs for specific rounding the integration of AI in educational settings. stakeholder groups to the implementation of particular Furthermore, pre-prepared questions guided the discusmain EVs. This result could be used to provide the ENs sions, enabling the researchers to gather qualitative data for specific stakeholder sets to implement a given main on the ENs and perspectives held by each stakeholder EV. group. Through these questions, particular emphasis
In addition to answering the two research questions, was placed on addressing gaps identified through the the following points of discussion were highlighted by SLR conducted prior to this study. the SLR: The qualitative data collected through the focus groups will be coded deductively with a-priori coding [18] using 1. Ethics should be included in the design of AIED thematic analysis. The six steps of thematic analysis 2. Ethics of AIED should focus on the educational described by Braun and Clarke will be followed with the aptness of AIED solutions software tool Dedoose [ 20]. The starting coding tree for 3. More ENs should be established for end users of the thematic analysis will be based on the results of the
AIED SLR. If any topics are identified through the thematic 4. There exists a tight coupling between EVs, leading analysis that do not fit within this coding tree, they will to possible ethical dilemmas be added inductively to the coding tree. The ENs and The main EVs and ENs identified in this SLR could viewpoints elicited from the focus group discussions will serve as a foundation for developing an ethical frame- serve as valuable inputs for the subsequent stages of the research project. orous and multifaceted approach, drawing upon various
The stakeholder consultation aims to gather input from data sources and stakeholder perspectives. the various stakeholder groups involved in AIED regard- The initial phase of the study will involve a systematic ing the ethical considerations and challenges associated literature review (SLR), which will serve as the foundawith using AIED. This input will then be integrated into tion for identifying the main ethical values (EVs) and the development of an ethical framework for AIED. More- ethical norms (ENs) pertinent to the domain of AIED. over, the data analysis process will involve mapping the This extensive review will consolidate and synthesize the ENs identified by stakeholders onto the main EVs for existing knowledge base, providing a theoretical groundAIED. This process will also verify the comprehensive- ing for the subsequent stages of the research. ness of the main EVs and supplement the ethical frame- Building upon this foundation, the second study enwork with any missing EVs and ENs pertaining to AIED. gage will key stakeholder groups through a series of focus group discussions. This qualitative approach will allow 2.3. Expert consultation for an in-depth exploration of the perspectives, concerns, and expectations of students, teachers, and educational This research study will engage an expert panel compris- administrators regarding the ethical implications of AIED. ing individuals with extensive knowledge and expertise The insights gathered from these discussions will enrich in the domain of AIED. The panel will consist of five to the understanding of the ethical landscape of AIED. ten experts. Furthermore, in a third study, we seek the expertise
The primary objective of this study is to leverage the of a panel of domain specialists to validate, rank and collective expertise of the panel in evaluating and pri- refine the proposed ethical framework. The culmination oritizing ENs for an ethical framework for AIED. Based of these eforts would result in a comprehensive ethical on the methodology described by Sclater, the experts framework that addresses the ethical challenges posed will be tasked with rating the identified ENs on a three- by the usage of AIED. point scale, assessing their criticality to the proposed This ethical framework could be used by students, framework. The ratings will be categorized as follows: 1) teachers and educational administrators to identify ethiCritical, 2) Important, and 3) Less important. This eval- cal issues in the usage of AIED systems. It could ensure uation will facilitate the selection of the most pertinent that the usage of AIED does not have any intentional or ENs, which will subsequently be incorporated into the unintentional adverse efects on their lives. Additionally, ethical framework. it underscores the importance of having a continuous
Furthermore, the study will solicit input from the ex- dialogue about the potential ethical issues caused by the pert panel regarding the optimal format and presentation rapid evolution of AIED systems. By prioritizing ethiof the final ethical framework. This consultation will en- cal considerations, the educational sector can harness sure that the framework is not only theoretically robust the transformative potential of AI while safeguarding but also practical and accessible, facilitating its efective the well-being, privacy, and fundamental rights of all dissemination and adoption within the AIED commu- stakeholders involved. nity and related stakeholders. It is to be noted that the detailed design of this study is still under progress.
The expert consultation will serve to refine and distill Acknowledgements the ENs identified through the preceding studies, concentrating on the most critical and essential ones. This I would like to thank my PhD supervisors - Dr. Corrie refinement process aims to prevent the resulting ethical Urlings, Dr. Giel van Lankveld and Prof. Dr. Roland framework from becoming overly intricate or overwhelm- Klemke - for their unwavering support and guidance, and ing for stakeholders to use efectively. Concurrently, the for always having my back through my PhD trajectory. insights and input garnered from the experts will guide the shaping of the framework into a format that is both References usable and comprehensive, ensuring its practical applicability and completeness.
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