Chemistry is often perceived as a demanding and abstract subject throughout the
educational pathway. For this reason, chemistry teachers, among other strategies,
support instruction through ICT-based teaching approaches. With the emergence of
generative artificial intelligence (GenAI), a new question arises: whether such tools can
meaningfully and responsibly enrich both the learning and teaching of chemistry in
upper-secondary education. This master’s thesis empirically examined the suitability
of using GenAI as a form of student support in the learning process. The study
investigated whether GenAI contributed to a better understanding of chemistry learning
content and improved learning outcomes, whether it enhanced the durability of
knowledge, how it influenced students’ interest and perceived competence, and what
attitudes students held toward GenAI and its use in chemistry lessons. The study
included a purposive sample of second-year general upper-secondary school students
from the Central Slovenia region (N = 27). As part of chemistry lessons, the students
carried out two laboratory exercises within the thematic unit Equilibria in Aqueous
Solutions. They were divided into two heterogeneous groups. For the first laboratory
exercise, one group prepared in the traditional way, while the other prepared
traditionally with the addition of personalised GenAI support. For the second laboratory
exercise, the preparation methods were reversed between the groups. After each
laboratory exercise, the students completed a knowledge test and questionnaires
consisting of Likert-type statements measuring situational interest, perceived
competence, and attitudes toward the use of GenAI. Two weeks later, a delayed
knowledge test was administered, and ten selected students participated in semi
structured interviews about their experiences with using GenAI in chemistry learning
and instruction. The results showed that, when preparing with the support of GenAI,
students achieved statistically significantly better results on both immediate knowledge
tests than when preparing in the traditional way. A similar pattern was also observed
in the delayed knowledge tests, suggesting that the use of GenAI contributed to greater
knowledge retention. Students also reported higher situational interest and greater
perceived competence in laboratory work when using GenAI. With regard to attitudes
toward GenAI, positive changes were particularly evident in students’ perceptions of
the usefulness of GenAI for learning and chemistry instruction. The qualitative findings
indicated that students generally perceived GenAI as a fast, accessible, and useful
form of learning support. They reported that it helped them understand concepts, check
answers, follow step-by-step explanations, and prepare for laboratory work. At the
same time, they pointed out several limitations, including the possibility of inaccurate
or insufficiently focused responses, the need for appropriate prior knowledge, the
difficulty of formulating high-quality prompts, and dilemmas concerning the boundary
between acceptable learning support and an inappropriate shortcut.
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