The doctoral dissertation in the field of Computer Science will be examined at the Faculty of Science, Forestry and Technology, Joensuu campus.
What is the topic of your doctoral research? Why is it important to study the topic?
My doctoral research examines how generative artificial intelligence (GenAI) can be integrated into programming education in ways that support higher-order thinking skills, such as problem-solving, critical thinking, and creativity, as well as students’ understanding of programming logic. GenAI can support activities such as code explanation, debugging, code generation, and feedback, but unguided use may encourage overreliance and reduce opportunities for students to reason independently.
This topic is important because GenAI is becoming increasingly common in programming education, creating a need for pedagogical approaches that help students benefit from these tools while remaining actively engaged in reasoning, problem-solving, and responsible learning.
What are the key findings or observations of your doctoral research?
The research suggests that the educational value of generative artificial intelligence (GenAI) in programming is influenced by how its use is structured. A review of 40 empirical studies identified recurring gaps in scaffolding, assessment, responsible use, accessibility, and alignment with learning objectives. These findings informed the development of the GenAI-Ped framework. In a seven-week study with 25 students, participants showed short-term improvements in higher-order thinking skills such as problem-solving, critical thinking, and creativity, as well as programming logic.
In addition, a controlled study with 124 students found higher post-test higher-order thinking skills scores and a modest advantage in programming logic among students who used GenAI-Ped compared with those who used GenAI without the GenAI-Ped framework. Interaction logs also indicated stronger pedagogical alignment, cognitive engagement, and responsible-use behaviours in students using the GenAI-Ped framework. What is new and valuable is the combination of a structured, empirically examined framework with a 28-item HOTS–Programming Logic Rubric that helps educators guide GenAI use and assess higher-order thinking skills and programming logic beyond code correctness.
How can the results of your doctoral research be utilised in practice?
The results can help educators, universities, and curriculum designers use GenAI in programming courses in a more structured way. The GenAI-Ped framework can guide the design of activities that require students to analyse problems, develop, and refine prompts, test, and debug AI-generated code, verify outputs, explain their decisions, and reflect on their learning. The 28-item HOTS–Programming Logic Rubric can also help educators assess problem-solving, critical thinking, creativity, and programming logic rather than relying only on whether code is correct. The framework is not tied to a single programming language or GenAI tool, although further research is needed to evaluate its use across additional languages, institutions, and educational contexts. As these tools can support course design, assessment, and responsible GenAI use in programming education.
What are the key research methods and materials used in your doctoral research?
The research was conducted through three linked studies using design science research and a pragmatic sequential mixed-methods approach. Study I systematically reviewed 40 empirical studies on GenAI in programming education to identify gaps and derive the design requirements for the GenAI-Ped framework.
Study II implemented the framework in a seven-week C++ intervention with 25 undergraduate computer science students and used a 28-item HOTS–Programming Logic Rubric to assess problem-solving, critical thinking, creativity, and programming logic.
Study III evaluated the framework in a controlled Java study with 124 students, with 62 students using GenAI-Ped and 62 using GenAI without the framework. Data included pretest and post-test assessments, learner feedback, code submissions, and GenAI interaction logs, which were analysed using quantitative and qualitative methods.
Is there something else about your doctoral dissertation you would like to share in the press release?
The dissertation highlights that the key question in programming education is no longer whether students will use generative artificial intelligence (GenAI), but how they can use it without weakening higher-order thinking skills (HOTS) such as problem-solving, critical thinking, and creativity, or their understanding of programming logic. The Generative Artificial Intelligence Programming Education (GenAI-Ped) framework was developed to address this challenge by positioning GenAI as a structured learning support rather than a replacement for reasoning.
The research also developed a 28-item HOTS–Programming Logic Rubric that helps educators assess students’ problem-solving, critical thinking, creativity, and programming logic beyond simply checking whether code is correct. The broader message is that effective GenAI integration in education requires pedagogy to guide the technology, rather than allowing the technology to determine how students learn.
The doctoral dissertation of Jemimah Nathaniel, MSc, entitled Generative Artificial Intelligence into programming education to support higher-order thinking skills and programming logic will be examined at the Faculty of Science, Forestry and Technology, Joensuu campus. The opponent will be Assistant Professor Juho Leinonen, Aalto University, and the custos will be Staff Scientist Jarkko Suhonen, University of Eastern Finland. Language of the public defence is English.
For further information, please contact:
Jemimah Nathaniel, [email protected], tel. +234 816 598 9836
