Publications by Tatjana Nazaretsky

The critical role of trust in adopting AI-powered educational technology for learning: An instrument for measuring student perceptions

Tanja Christina Käser Jacober, Paola Mejia Domenzain, Vinitra Swamy, Jibril Albachir Frej, Tatjana Nazaretsky

In recent decades, we have witnessed the democratization of AI-powered Educational Technology (AI-EdTech). However, despite the increased accessibility and evolving technological capabilities, its adoption is accompanied by significant challenges, predomin ...

Elsevier BV2025

How Instructional Sequence and Personalized Support Impact Diagnostic Strategy Learning

Tanja Christina Käser Jacober, Bahar Radmehr, Tatjana Nazaretsky

Supporting students in developing effective diagnostic reasoning is a key challenge in various educational domains. Novices often struggle with cognitive biases such as premature closure and over-reliance on heuristics. Scenario-based learning (SBL) can ad ...

Springer2025

Leveraging Knowledge Profiles and Generative AI for Realistic Student Response Generation

Tanja Christina Käser Jacober, Tatjana Nazaretsky

The scarcity of high-quality labeled data often hinders the effective use of automated formative feedback in education. While analytic rubrics offer a reliable framework for automated grading, training robust models still requires hundreds of expert-labele ...

Springer Nature2025

Enhancing Procedural Writing Through Personalized Example Retrieval: A Case Study on Cooking Recipes

Tanja Christina Käser Jacober, Paola Mejia Domenzain, Luca Mouchel, Antoine Bosselut, Thiemo Wambsganss, Seyed Parsa Neshaei, Jibril Albachir Frej, Tatjana Nazaretsky

Writing high-quality procedural texts is a challenging task for many learners. While example-based learning has shown promise as a feedback approach, a limitation arises when all learners receive the same content without considering their individual input ...

2024

Explainable AI for Unsupervised Machine Learning: A Proposed Scheme Applied to a Case Study with Science Teachers

Tatjana Nazaretsky

Explainable Artificial Intelligence (XAI) seeks to render Artificial Intelligence (AI) models transparent and comprehensible, potentially increasing trust and confidence in AI recommendations. This research explores the realm of XAI within unsupervised edu ...

Science and Technology Publications, Lda2024

Exploring the Potential of Automated and Personalized Feedback to Support Science Teacher Learning

Tatjana Nazaretsky

This study investigates how automated and personalized feedback can support elementary teachers’ learning within digital teaching simulations. We interviewed 15 participants to elicit their understanding and evaluation of feedback about a teacher’s facilit ...

Springer Science and Business Media Deutschland GmbH2024

Navigating Self-regulated Learning Dimensions: Exploring Interactions Across Modalities

Tanja Christina Käser Jacober, Paola Mejia Domenzain, Tatjana Nazaretsky

Self-regulated learning (SRL) has been extensively studied using self-reported measures, such as surveys, and more recently, behavioral measures, such as trace data. While both modalities offer insights into SRL, their relationship remains ambiguous. Altho ...

Springer Nature Switzerland2024

AI or Human? Evaluating Student Feedback Perceptions in Higher Education

Tanja Christina Käser Jacober, Paola Mejia Domenzain, Vinitra Swamy, Jibril Albachir Frej, Tatjana Nazaretsky

Feedback plays a crucial role in learning by helping individuals understand and improve their performance. Yet, providing timely, personalized feedback in higher education presents a challenge due to the large and diverse student population, often resultin ...

Springer Nature Switzerland2024

Finding Paths for Explainable MOOC Recommendation: A Learner Perspective

Tanja Christina Käser Jacober, Jibril Albachir Frej, Tatjana Nazaretsky

The increasing availability of Massive Open Online Courses (MOOCs) has created a necessity for personalized course recommendation systems. These systems often combine neural networks with Knowledge Graphs (KGs) to achieve richer representations of learners ...

Assoc Computing Machinery2024

Finding Paths for Explainable MOOC Recommendation: A Learner Perspective

Tanja Christina Käser Jacober, Jibril Albachir Frej, Tatjana Nazaretsky

The increasing availability of Massive Open Online Courses (MOOCs) has created a necessity for personalized course recommendation systems. These systems often combine neural networks with Knowledge Graphs (KGs) to achieve richer representations of learners ...

2023