SUPERVISOR: pantelis papadopoulos
topic
The recent advances in Machine Learning and Deep Learning have increased the potential of Artificial Intelligence (AI) rapidly, making it omnipresent in several aspects of our daily lives. Yet, this technological progress has revealed the need for further analysis on the shortcoming and the ethical use of AI.
For example, in machine learning systems, there is first a training phase in which the system “learns” to detect patterns by examining a vast amount of data (thousands of cat images to learn what a cat looks like). Once the system is fully trained to detect the right patterns and act accordingly, it is tested with new data. If its performance is good, then it is deployed for use in real settings. Since the training phase cannot cover all possible variations, an unexpected new image can fool the system. Inequality or bias hidden in training data can severely affect how an AI system will act when predicting the success of people with different gender, race, or socioeconomic status. Selecting the right dataset to train a model that will accurately depict the real world and make decisions is not a straightforward process as biases and inequality may be endemic to the field.
There is a need for standards for the use of AI in Education and several guidelines have already been proposed in the literature such as transparency/explainability of data (i.e., ensuring algorithmic accountability), justice and fairness (i.e., promoting equitable and inclusive use of AI in Education), and privacy.
Some of the possible research questions within this topic are (a) how transparency and explainability of AI in Education affect its acceptance by students and teachers, (b) how can AI bias affect learning, and (c) at what degree AI decision-making is aligned with learning theories?
METHOD
Depending on the research question and the targeted audience (individual students, groups, teachers), the study may be based on a comparative analysis of different study conditions or the impact of appropriate intervention within the same condition.
references
Holmes, W., Porayska-Pomsta, K., Holstein, K. et al. Ethics of AI in Education: Towards a Community-Wide Framework. Int J Artif Intell Educ (2021). https://doi.org/10.1007/s40593-021-00239-1
Adams C., Pente P., Lemermeyer G., Rockwell G. (2021) Artificial Intelligence Ethics Guidelines for K-12 Education: A Review of the Global Landscape. In: Roll I., McNamara D., Sosnovsky S., Luckin R., Dimitrova V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science, vol 12749. Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_4
Borenstein, J., Howard, A. Emerging challenges in AI and the need for AI ethics education. AI Ethics 1, 61–65 (2021). https://doi.org/10.1007/s43681-020-00002-7