Publikation
Towards a Mapping for AI-Based Eye-Tracking Analytics in Multimedia Learning
Konstantinos Tsiakas; Francesca Zermiani; Deniz Iren; Nghia Duong-Trung; Michael Raschke; Roland Klemke; Milos Kravcik; Ladislao Salmerón; Halszka Jarodzka; Leen Catrysse
In: Joshua Weidlich; Irene-Angelica Chounta; Kairit Tammets; Tanya Nazaretsky; Bibeg Limbu; Fridolin Wild; Pablo Arnau-González (Hrsg.). Mindful TEL: Learning Technologies Shaped with Intention. European Conference on Technology Enhanced Learning (EC-TEL-2026), 21st European Conference on Technology Enhanced Learning, ECTEL 2026, September 14-18, Valencia, Spain, Lecture Notes in Computer Science (LNCS), Vol. 16905, ISBN 978-3-032-37978-8, Springer, Cham, 9/2026.
Zusammenfassung
Supporting students’ reading comprehension is demanding
for teachers, as they have limited insight into the reading processes of
each student while they are reading. Although eye-tracking and artificial
intelligence (AI) approaches offer strong potential to generate such
insights, they remain difficult to deploy and apply in actual classroom
practice. A key limitation lies in the lack of a structured approach to
translate gaze data into pedagogically sound, classroom-ready, actionable
insights for teachers. In this paper, we address this gap with a
proof-of-concept mapping that connects the Cognitive Theory of Multimedia
Learning (CTML) processes—selecting, organizing, and integrating—
and their associated eye-tracking metrics with specific AI methods
and HCI-driven visualizations. In this ongoing work, we present an initial
mapping derived from a literature review: we identify eye-tracking
metrics associated with CTML processes and propose preliminary connections
to AI methods and HCI-driven visualizations. Drawing on this
mapping, we illustrate our approach with a concrete example from a
PISA task. We argue that this structured approach is a necessary step
toward designing more targeted applications, moving from gaze data to
feedback that teachers can use. By grounding technical and design choices
in CTML, this mapping aims to support the development of AI-enhanced
tools that are technically sound and pedagogically relevant.
