Robot programming skill classes are becoming more popular. Higher order thinking, on the other hand, is an important issue in developing the skills of 21st-century learners. Truth be told, those two abilities are consistent subjects that are trending in academics. The purpose of this study is to design the components and indicators of a robot programming skill assessment based on higher order thinking. The methodology is divided into two phases: (1) qualitative research: a review of the literature on the issues for the synthesis of components and indicators of the robot programming skill assessment based on higher order thinking; and (2) quantitative research: to test the validity of the robot programming skill assessment by the content validity index test (CVI) with seven experts and the reliability with Cronbach’s alpha statistic test with the questionnaire results from 50 participants. The results show that the synthesized robot programming skill assessment consists of three components with 16 indicators, all of which are accepted for their agreed content validity index assessment (CVI = 1.00), and the internal consistency calculation results for the reliability test are found to have an acceptable reliability (α = 0.747).

Parkinson's disease, a neurodegenerative disorder characterized by tremors, significantly impedes daily activities, including the simple act of eating. This impairment is particularly evident when consuming dishes like noodles, where precise utensil control becomes a challenge. Recognizing the intricate connection between Parkinson's symptoms and the prevalence of noodle-based cuisines worldwide, this project aims to develop self-stabilizing spinning utensils specifically designed to enhance the dining experience of Parkinson's patients. By minimizing tremor-induced utensil movements, these innovative utensils will promote independent eating and contribute to an improved quality of life for individuals with Parkinson's.

The deployment of small language models (SLMs) at the edge raises a fundamental behavioral question: under a fixed word budget, how do these models prioritize semantic content? This paper investigates content selection in two representative edge SLMs—gemma3n-e4b and Llama-3.2-3B—under systematic word-budget compression when generating person descriptions in English and Thai. A 2 × 2 (model × language) factorial design yielded 9360 observations across 18 profiles, 13 budget levels (1–25 words, odd-step intervals), and 10 trials per cell. Output richness was quantified via an Information Density Score (IDS), a binary annotation-based metric capturing seven attributes: name, occupation, gender, education, income, and two personality traits. Results confirm strong, generally increasing IDS–budget relationships across all conditions (Pearson r = 0.663–0.903). In English, gemma3n-e4b reached every key threshold exactly four words ahead of Llama-3.2-3B (B50: w = 7 vs. 11; B70: w = 13 vs. 17; B80: w = 17 vs. 21). In Thai, both models converged at B50 (w = 9), but gemma retained a four-word lead at B70 (w = 13 vs. 17), while Llama never reached B80 (max IDS = 0.796 at w = 25). Occupation was the invariant anchor across all conditions (retention ≥ 0.969). Models diverged on secondary attributes: gemma suppressed Gender (4.8% English, 24.2% Thai), while Llama deprioritized Education (23.6% English, 15.2% Thai; 0.267 at w = 25 in Thai). In Thai, gemma’s personality traits overtook Income in the salience hierarchy—a reordering absent in English. Gemma showed 2.5–3.8× higher within-trial consistency than Llama in both languages. These findings indicate that content selection under budget pressure is determined primarily by model architecture rather than linguistic context, and that Thai reduces but does not eliminate the cross-architecture compression efficiency gap.

Decades-old enterprise-resource-planning (ERP) systems lock operational data inside unstructured, human-readable reports, forcing slow, costly, error-prone manual re-keying. Because multimodal large language model (MLLM) capability is uneven, deploying MLLMs for extraction means trusting outputs without a labeled reference. We test this with a within-document controlled experiment on 400 controlled-substance stock-ledger documents (2951 records, 11 fields, predominantly Thai) from a Thai pharmaceutical factory, comparing trained human double-entry against four open-weight MLLMs (2 × 2 design: vendor × architecture) via OpenRouter. Human double-entry left 14 discrepancies against the adjudicated gold standard, none common to both operators. The strongest model, Qwen3-VL-32B-Instruct (Dense), reached 93.95% cell accuracy; among these four models, field accuracy varied more across vendors, whereas structural completeness differed consistently between dense models (0 missing records) and Mixture-of-Experts models (up to 51 of 2951 dropped). Deterministic accounting invariants flagged 0.61% of its records, leaving the unflagged majority 94.1% accurate across all 11 fields; adding calendar rules flagged 4.61% and raised residual date accuracy from 92.1% to 95.9%. We report both operating points and recommend the extended level where date fidelity is regulatory-critical. The pipeline is 13.5–29.4× faster in wall-clock terms and 97.5–99.5% cheaper. Gold-free, rule-based verification thus locates where MLLM reliability holds, giving human–AI collaboration quantified, disclosed residual risk rather than an implied guarantee. Even at the more conservative operating point, unflagged records average 94.5% accuracy across all 11 fields but only 43.7% on the free-text Remarks field, which the triage cannot check; the results support risk reduction and the localization of review effort, not unrestricted regulatory reliability across all fields.