Abstract
Mentoring is a highly personal and individual process, in which mentees take advantage of expertise and experience to expand their knowledge and to achieve individual goals. The emerging use of AI in mentoring processes in higher education not only necessitates the adherence to applicable laws and regulations (e.g., relating to data protection and non-discrimination) but further requires a thorough understanding of ethical norms, guidelines, and unresolved issues (e.g., integrity of data, safety, and security of systems, and confidentiality, avoiding bias, insuring trust in and transparency of algorithms). Mentoring in Higher Education requires one of the highest degrees of trust, openness, and social–emotional support, as much is at the stake for mentees, especially their academic attainment, career options, and future life choices. However, ethical compromises seem to be common when digital systems are introduced, and the underlying ethical questions in AI-supported mentoring are still insufficiently addressed in research, development, and application. One of the challenges is to strive for privacy and data economy on the one hand, while Big Data is the prerequisite of AI-supported environments on the other hand. How can ethical norms and general guidelines of AIED be respected in complex digital mentoring processes? This article strives to start a discourse on the relevant ethical questions and in this way raise awareness for the ethical development and use of future data-driven, AI-supported mentoring environments in higher education.
Highlights
Mentoring is widely accepted as a very beneficial, personal, and individual support, in which mentees take advantage of expertise and experience to expand their knowledge and to achieve individual goals
The emerging use of AI in mentoring processes in higher education necessitates the adherence to applicable laws and regulations and further requires a thorough understanding of ethical norms, guidelines, and unresolved issues
We identified principles that are unique in both contexts. Because of their focus on the personal relationship, the mentoring ethics principles, listed on the left, do not concentrate on the aspects of “technical robustness” and “safety,” “sustainability” and “equivalent aspects,” “effectiveness,” and “democracy.” we argue that traditional face-to-face mentoring can benefit from considering AI ethics principles that raise more awareness of possible sensitivity of data, which is the basis for all mentoring relationships
Summary
Mentoring is widely accepted as a very beneficial, personal, and individual support, in which mentees take advantage of expertise and experience to expand their knowledge and to achieve individual goals. A) In their article, “The Ethics of Mentoring,” Moberg and Velazquez (2004) develop a set of ethical responsibilities of mentors They argue that mentors have more responsibilities in the mentoring relationship than mentees, because they assume a “quasi-professional role”; provide “the protégé with the benefits of knowledge, wisdom, and developmental support”; and typically wield “superior power” (ibid.). They propose a model based on utilitarian principles (maximizing the good and minimizing harm), rights principles (taking into consideration individuals’ rights to be treated as a free and rational person), additional principles of justice (equitizing the distribution of benefit and burden), and principles of caring (even arguing for a certain degree of “legitimate” partiality). These four ethical principles imply the following seven mentor obligations: 1. Beneficence: to do good, to provide knowledge, wisdom, and developmental support to mentees
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