This study evaluates the performance of six generative artificial intelligence (AI) systems in solving Thai-language multiple-choice examinations in the subject of Programmable Logic Controllers (PLC), a core component of Industrial Electrical Engineering education. Six large language models (LLMs), including ChatGPT, Claude, DeepSeek, Gemini, Copilot, and Grok, were tested using fifteen sets of PLC examination questions. Statistical analysis was conducted using one-way ANOVA and two-sample t-Tests with Bonferroni correction to examine performance differences. In addition, effect size measures, including Eta-squared (η²) and Cohen‟s d, were calculated to assess the magnitude of the observed differences. The results show that ChatGPT achieved the highest mean score (77.27%), while DeepSeek followed closely (76.73%) and demonstrated the lowest standard deviation (±1.83%), indicating the most consistent performance across test sets. Claude also performed strongly (74.80%), whereas Gemini, Copilot, and Grok achieved similar mid-tier scores ranging from 72.40% to 72.73%. Although all LLMs achieved scores within the passing grade range, ANOVA confirmed statistically significant differences among systems (p-value is 0.0002). However, after applying the Bonferroni correction, only a subset of pairwise differences remained statistically significant, particularly between DeepSeek and several mid-tier LLMs, while the differences among ChatGPT, Claude, and DeepSeek were not statistically significant under the adjusted threshold. Effect size analysis further indicates that some of these differences represent meaningful practical variation in LLM performance. These findings indicate that contemporary LLMs demonstrate baseline comprehension of PLC concepts and can achieve passing-level performance in technical examinations conducted in a non-English language. The study contributes empirical evidence on AI performance in Thai-language technical assessments and highlights the potential role of generative AI as a complementary learning support tool in vocational and engineering education.

This paper presents the performance evaluation of 3G mobile networks for one kind of multimedia application called Voice over IP (VoIP) within Bangkok, the capital city of Thailand, using Line and Skype, two popular VoIP applications. This study used evaluation of voice quality provided by both applications. The tests have been conducted using stationary scenarios over 5 major 3G mobile networks, served by 5 operators, within 14 universities in the inner city of Bangkok in order to gather data of degraded speech files. Then, the data was measured using Perceptual Evaluation of Speech Quality (PESQ) to find Mean Opinion Score (MOS) before analyzing with ANOVA and T-test, which are statistical tools, so that the discussion and conclusion can be eventually derived.

This paper presents the comparison between two codecs, G.711 and G.722 at 64 kbps, referring to speech quality perception using a subjective method called interview tests. These subjective tests have been conducted with 201 subjects, who are Thai native speakers that use Thai which is a tonal language, for accuracy and reliability of results. The results from testing with both codecs are almost the same; the scores are 4.17 for G.722 and 4.14 for G.711. After analyzing the results, it has been confirmed that G.722 does not provide better speech quality than G.711 to the Thai subjects significantly, which is consistent with previous information. However, these results could be used as the benchmark of G.711 and G.722 for speech quality assessment within Thai environments.