In recent years, artificial intelligence (AI) has emerged as a valuable resource for teaching and learning, and it has also shown promise as a tool to help solve problems. A tool that has gained attention in education is ChatGPT, which supports teaching and learning through AI. This research investigates the difficulties faced by ChatGPT in comprehending and responding to chemistry problems pertaining to the topic of Introduction to Material Science. By employing the theoretical framework proposed by Holme et al., encompassing categories such as transfer, depth, predict/explain, problem solving, and translate, we evaluate ChatGPT’s conceptual understanding difficulties. We presented ChatGPT with a set of thirty chemistry problems within the Introduction to Material Science domain and tasked it with generating solutions. Our findings indicated that ChatGPT encountered significant conceptual knowledge difficulties across various categories, with a notable emphasis on representations and depth, where difficulties in representations hindered effective knowledge transfer.

In recent years, the integration of technology into education has significantly transformed teaching methods, especially in science education. Tools like PhET simulations have proven highly effective in enhancing student engagement and comprehension. Research has highlighted the value of simulation-based learning in fostering critical thinking and problem-solving skills. This study aimed to explore the impact of simulations, with a focus on PhET, on improving elementary students’ learning outcomes, an area that remains under-researched. The study compared the performance of two groups of third-grade students: one group learned about solubility using PhET simulations, while the other relied on traditional textbook instruction. Each group comprised fifty students. The study lasted for a two-month period. The instructional approach was investigative learning. Data were gathered through student responses to materials science questions aligned with Bloom’s Taxonomy, allowing for a detailed evaluation of their understanding and application of scientific concepts. Responses were assessed for accuracy and scored accordingly. We ran an independent-sample t-test to decide whether the difference in the mean score in science achievement between the two research groups was significant. The results showed that students using PhET simulations not only achieved significantly higher scores but also demonstrated their ability to explain their reasoning during problem-solving tasks. These findings emphasize the substantial advantages of incorporating digital tools like PhET simulations into elementary science education, as they enhance conceptual understanding and better equip students to tackle future scientific challenges. The present research results complement the previous research on using technology in the chemistry elementary classroom and add the issue of simulations to this research. The results of this study are centered on the topic of solubility. To broaden the generalizability of these findings, future research should examine the effects of simulations on student achievement in a wider range of elementary science topics.

This research delved into the integration of PhET simulations in elementary science education, specifically aimed at Grade 3 students. The primary objective was to evaluate how the use of these digital simulations influenced students’ conceiving of scientific concepts, focusing on “States of M1atter and Phase Changes” and “Solubility and Saturation”. Employing a qualitative research approach, the study observed 19 students who worked in pairs and trios as they engaged with PhET simulations to explore assigned science topics and address related questions. The observations centered on tracking students’ interactions with simulations and their progression through different knowledge phases. We used deductive and inductive content analysis to analyze the transcripts of the observation. The findings reveal that in the “Remembering” phase, students demonstrated a tendency to relate personal experiences to simulations, underscoring real-life context’s role in learning. The “Understanding” phase highlighted how PhET simulations facilitated deeper comprehension, with students making insightful observations. Additionally, the “Application” phase showcased the effective translation of simulation-derived knowledge into practical scenarios, bridging theoretical and real-world understanding. Students’ use of high-order thinking skills, at the analysis, evaluation, and creative phases, showed that simulations supported Grade 3 students in their learning processes of scientific concepts. The research underscores the efficacy of integrating PhET simulations into elementary science education, enhancing students’ knowledge by promoting active engagement and problem-solving skills. Integrating simulations into teaching methodologies emerges as a promising avenue to nurture scientific expertise and holistic understanding among elementary school students.

Generative artificial intelligence tools are flourishing across various sciences, particularly in the educational domain. ChatGPT, with its versions 3.5 and 4.0, stands out for its multilingual capabilities, catering to both students and educators. This research aimed to evaluate the performance differences between these versions, and importantly, to compare the accuracy of ChatGPT's responses in English versus Arabic. In our study, we presented ChatGPT with 39 chemistry problems related to 6th-7th grade curriculum, including twenty-five open questions and fourteen multiple-choice questions. Each response was categorized as accurate, partially accurate, or inaccurate. Our analysis, focused on trends across versions and languages, revealed significant enhancements in version 4.0, particularly in its ability to process Arabic. However, despite these improvements, our findings indicate that responses in English consistently outperformed those in Arabic in terms of accuracy. In light of this discrepancy, we suggest that ChatGPT either incorporates additional Arabic databases into its training regimen or develops a code that adopts the following strategy to ensure more accurate responses in science education: questions should first be translated from Arabic to English, addressed, and then retranslated into Arabic before being presented to the user. This approach takes advantage of the greater accuracy of responses in English to benefit Arabic-speaking users, and it may also improve outcomes for speakers of other languages who experience less precise answers in the fields of chemistry or science education.