TY - GEN
T1 - Seeing the Forest from the Trees
T2 - 19th European Conference on Technology Enhanced Learning, EC-TEL 2024
AU - Feldman-Maggor, Yael
AU - Cerratto-Pargman, Teresa
AU - Viberg, Olga
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - Artificial intelligence (AI) holds significant promise as a technology that may improve the quality of educational practices. This includes specialized AI-powered technologies tailored for education and general AI-based technologies, including recently popular generative AI tools that stakeholders are increasingly adapting for teaching and learning. Integrating AI tools into educational settings holds numerous potential pedagogical benefits, such as assisting teachers in planning lessons, promoting personalization, and enhancing student autonomy. However, concerns about bias and discrimination linked to the use of these technologies have rapidly emerged. Today, standardized evaluation criteria to assess the potential contribution of such tools to education and their reliability within the learning and teaching context are lacking. To address this gap, we build on an existing taxonomy for the evaluation of open educational resources (OER) to better suit the unique features of generative AI. The result is a six-dimensional evaluation approach that includes descriptive, pedagogical, representational, communication, scientific content, as well as the ethical and transparency dimension. We then apply this approach to examine the educational potential and ethical concerns around 30 AI tools. The analysis facilitates a critical mapping of the potential and risks of AI-powered technologies in education settings.
AB - Artificial intelligence (AI) holds significant promise as a technology that may improve the quality of educational practices. This includes specialized AI-powered technologies tailored for education and general AI-based technologies, including recently popular generative AI tools that stakeholders are increasingly adapting for teaching and learning. Integrating AI tools into educational settings holds numerous potential pedagogical benefits, such as assisting teachers in planning lessons, promoting personalization, and enhancing student autonomy. However, concerns about bias and discrimination linked to the use of these technologies have rapidly emerged. Today, standardized evaluation criteria to assess the potential contribution of such tools to education and their reliability within the learning and teaching context are lacking. To address this gap, we build on an existing taxonomy for the evaluation of open educational resources (OER) to better suit the unique features of generative AI. The result is a six-dimensional evaluation approach that includes descriptive, pedagogical, representational, communication, scientific content, as well as the ethical and transparency dimension. We then apply this approach to examine the educational potential and ethical concerns around 30 AI tools. The analysis facilitates a critical mapping of the potential and risks of AI-powered technologies in education settings.
KW - Algorithm Bias
KW - Generative AI
KW - Open Educational Resource (OER)
UR - https://www.scopus.com/pages/publications/85205311849
U2 - 10.1007/978-3-031-72312-4_12
DO - 10.1007/978-3-031-72312-4_12
M3 - Conference contribution
AN - SCOPUS:85205311849
SN - 9783031723117
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 99
EP - 105
BT - Technology Enhanced Learning for Inclusive and Equitable Quality Education - 19th European Conference on Technology Enhanced Learning, EC-TEL 2024, Proceedings
A2 - Ferreira Mello, Rafael
A2 - Rummel, Nikol
A2 - Jivet, Ioana
A2 - Pishtari, Gerti
A2 - Ruipérez Valiente, José A.
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 16 September 2024 through 20 September 2024
ER -