I am a Postdoctoral Researcher at Goethe University Frankfurt, Germany, and the DIPF | Leibniz Institute for Research and Information in Education. My work focuses on the intersection of responsible Artificial Intelligence (AI), Learning Analytics (LA), and educational innovation.
In my current research, I integrate responsible AI practices into the design and application of educational technologies (EdTech). This includes supporting educators and institutions with the use of LA, guiding the selection of meaningful indicators, and contributing to evidence-based course design.
A major part of my work involves exploring the potential of Large Language Models (LLMs) to enhance student assessment and personalized feedback. I investigate how LLMs can improve the quality of extracted knowledge from educational data and how this knowledge can be transformed into actionable insights that foster effective teaching and learning.
Ultimately, my goal is to bridge cutting-edge AI research with practical educational applications, ensuring that innovations are not only technically robust but also ethical, transparent, and beneficial for both learners and educators.
I completed my PhD with a thesis titled “Uncharted Territories: Learning Analytics Indicators from a Learning Design Perspective”, in which I developed the Learning Analytics and Design Alignment (LADA) framework to systematically align Learning Design and Learning Analytics. During my PhD, I developed and evaluated OpenLAIR, which is an evidence-based LA indiactor repository. Additionally, this work combined systematic literature reviews with advanced methods such as machine learning (NLP), visual analytics, and knowledge extraction to generate deeper insights.
Beyond research, I am actively involved in teaching at both the bachelor’s and master’s levels, including course instruction and thesis supervision. To date, I have supervised around 13 bachelor’s and master’s theses and am currently supervising PhD students.
• Postdoctoral Researcher at Goethe University Frankfurt and DIPF - Germany (2024 - Present)
• Research Associate at DIPF | Leibniz Institute for Research and Information in Education - Germany (2018 - 2024)
• Student Assistant at RWTH Aachen University - Germany (2016 - 2018)
• Software Engineer - Pakistan (2013 - 2015)
• PhD in Computer Science at Goethe University Frankfurt - Germany
• M.Sc. at RWTH Aachen University - Germany (2015 - 2018)
• B.Sc. at IIU Islamabad - Pakistan (2010 - 2014)
• Higher Secondary School Certificate - Pakistan (2008 - 2010) *(University Entrance Qualification)
• Secondary School Certificate - Pakistan (1996 - 2008)
Atezaz Ahmad
Eschersheimer Landstr. 155-157,
60323 Frankfurt am Main
Email 1: ahmad@studiumdigitale.uni-frankfurt.de
Email 2: a.ahmad@dipf.de
On October 1st, 2025, I successfully defended my PhD thesis, “Uncharted Territories: Learning Analytics Indicators from a Learning Design Perspective.”
In my PhD, I developed the Learning Analytics and Design Alignment (LADA) framework to systematically connect Learning Design (LD) with Learning
Analytics (LA). I also worked on OpenLAIR, a tool that helps course designers, teachers, students, and researchers select suitable learning activities,
analytics indicators, and metrics for course design and dashboards. To ensure OpenLAIR remains current, I explored natural language processing (NLP)
methods to automate the extraction of indicators and metrics from the literature.
Now, as I continue my academic journey, my research focuses on Responsible AI, applying Learning Analytics to enhance course design, and
integrating Large Language Models (LLMs) into my approaches to improve the quality and reliability of extracted knowledge-alongside my passion
for teaching.
I have submitted my Ph.D. thesis, titled “Uncharted Territories: Learning Analytics Indicators from a Learning Design Perspective. ” The thesis is currently under evaluation, and the doctoral degree is expected to be awarded by the end of August 2025.
I am in the process of composing my doctoral dissertation, which is the culmination of my PhD journey. It involves a deep dive into data analysis, literature review, and synthesis of ideas to form a coherent argument that supports my thesis.
OpenLAIR has introduced a novel feature that integrates a review and verdict system into its dashboard. The new review system allows users to rate and provide feedback on learning analytics indicators, fostering a collaborative environment where insights can be shared and discussed. Complementing this, the verdict system empowers users to assess the reliability of indicators, offering guidance on their trustworthiness based on comprehensive information provided within OpenLAIR. These features aim to streamline the selection process for course designers, educators, and researchers, ensuring that the most effective and relevant indicators are employed in educational settings.
The paper will be showcased during a presentation at the CSEDU 2024 conference, which is scheduled to be held in the beautiful city of Angers, France. This academic event provides an excellent platform for scholars and researchers to share their findings and engage in fruitful discussions within the field of computer science and education.
Full research paper titled "Students Want to Experiment While Teachers Care More About Assessment! Exploring How Novices and Experts Engage in Course Design." is accepted at CSEDU. CSEDU, the International Conference on Computer Supported Education, is a yearly meeting place for presenting and discussing new educational tools and environments, best practices and case studies on innovative technology-based learning strategies, and institutional policies on computer supported education including open and distance education. CSEDU will provide an overview of current technologies as well as upcoming trends, and promote discussion about the pedagogical potential of new educational technologies in the academic and corporate world. CSEDU seeks papers and posters describing educational technology research; academic or business case-studies; or advanced prototypes, systems, tools, and techniques.
We have developed an advanced API for OpenLAIR, designed to autonomously extract critical features such as indicators, metrics, activities, and events from the latest scholarly articles in learning analytics. This API leverages cutting-edge algorithms to parse and analyze academic texts, identifying key elements that contribute to the understanding and advancement of learning analytics. By automating this process, the API facilitates a more efficient and comprehensive review of literature, enabling stakeholders to quickly assimilate new findings and integrate them into their educational strategies and tools. This represents a significant leap forward in the field, streamlining research processes and enhancing the accessibility of valuable data for educators and researchers.
As part of the OpenLAIR project, I have developed a Natural Language Processing (NLP) tool designed to automatically extract key Learning Design–Learning Analytics (LD-LA) elements from newly published scholarly articles. This tool leverages advanced text mining techniques to identify relevant concepts, frameworks, and indicators, and systematically integrates the extracted insights into the OpenLAIR database. By automating this process, the tool ensures that OpenLAIR remains up-to-date with the latest research developments, supporting continuous improvement and knowledge discovery within the learning analytics community.
Should you have any inquiries or are interested in collaboration, feel free to reach out via email. I'm eager to respond and look forward to our interaction.