The ability to use complex tools is thought to depend on multifaceted motor-to-mechanical transformations within the left inferior parietal lobule (IPL), linked to cognitive control over compound actions. Here we show using neuroimaging that demanding transformations of finger movements into proper mechanical movements of functional parts of complex tools invoke significantly the right rather than left rostral IPL, and bilateral posterior-to-mid and left anterior intraparietal sulci. These findings emerged during the functional grasp and tool-use programming phase. The expected engagement of left IPL was partly revealed by traditional region-of-interest analyses, and further modeling/estimations at the hand-independent level. Thus, our results point to a special role of right IPL in supporting sensory-motor spatial mechanisms which enable an effective control of fingers in skillful handling of complex tools. The resulting motor-to-mechanical transformations involve dynamic hand-centered to target-centered reference frame conversions indispensable for efficient interactions with the environment.

This article presents a systematic review of empirical research on the use of large language models (LLMs) for automated grading of student work and providing feedback. The study aimed to determine the extent to which generative artificial intelligence models, such as ChatGPT, can replace teachers in the assessment process. The review was conducted in accordance with PRISMA guidelines and predefined inclusion criteria; ultimately, 42 empirical studies were included in the analysis. The results of the review indicate that the effectiveness of LLMs in grading is varied. These models perform well on closed-ended tasks and short-answer questions, often achieving accuracy comparable to human evaluators. However, they struggle with assessing complex, open-ended, or subjective assignments that require in-depth analysis or creativity. The quality of the prompts provided to the model and the use of detailed scoring rubrics significantly influence the accuracy and consistency of the grades generated by LLMs. The findings suggest that LLMs can support teachers by accelerating the grading process and delivering rapid feedback at scale, but they cannot fully replace human judgment. The highest effectiveness is achieved in hybrid assessment systems that combine AI-driven automatic grading with teacher oversight and verification.

Filozofowie i naukowcy odwołują się do szczególnego, oczywistego dla wszystkich świadomych ludzi charakteru świadomości fenomenalnej. Do niedawna nie dysponowaliśmy żadnymi ustaleniami empirycznymi na temat potocznego poglądu na temat świadomości. W ostatnich latach, w ramach eksperymentalnej filozofii świadomości, takie badania zostały przeprowadzone. Zgodnie z wiodącą interpretacją ich wyników, fenomenalny aspekt świadomości nie jest jednak dostrzegany przez laików, osoby bez wiedzy akademickiej na temat świadomości. Celem artykułu jest odpowiedź na pytanie czy możemy zaufać tym wynikom. Pokazuję, że istnieją poważne wątpliwości co do trafności przeprowadzonych badań. W konsekwencji, wiodąca interpretacja powinna zostać odrzucona, a tytułowe pytanie o potoczny charakter pojęcie świadomości pozostaje otwarte.

This article presents a systematic review of empirical research on the use of large language models (LLMs) for automated grading of student work and providing feedback. The study aimed to determine the extent to which generative artificial intelligence models, such as ChatGPT, can replace teachers in the assessment process. The review was conducted in accordance with PRISMA guidelines and predefined inclusion criteria; ultimately, 42 empirical studies were included in the analysis. The results of the review indicate that the effectiveness of LLMs in grading is varied. These models perform well on closed-ended tasks and short-answer questions, often achieving accuracy comparable to human evaluators. However, they struggle with assessing complex, open-ended, or subjective assignments that require in-depth analysis or creativity. The quality of the prompts provided to the model and the use of detailed scoring rubrics significantly influence the accuracy and consistency of grades generated by LLMs. The findings suggest that LLMs can support teachers by accelerating the grading process and delivering rapid feedback at scale, but they cannot fully replace human judgment. The highest effectiveness is achieved in hybrid assessment systems that combine AI-driven automatic grading with teacher oversight and verification.

Scholars of consciousness often view the “what it is like” phrase as an easy-to-understand strategy for introducing the topic of phenomenal consciousness. But do laypeople understand and use the phrase the way scholars do? The ordinary view assumes that the ubiquity of the phrase in philosophical writings reflects an alignment with everyday language, where it purportedly refers to phenomenal consciousness. In this article, I argue that the ordinary view is incorrect, as it overestimates laypeople’s conceptual grasp of phenomenality. Instead, I propose a weaker view: laypeople use “what it is like” to describe their subjective experiences in general terms, without committing to the concept of phenomenality. This approach offers a practical entry point for exploring how ordinary language and thinking about consciousness intersect with philosophical discourse.

There is probably no more popular cliché in the literature focused on consciousness than postulating the unique status of phenomenal consciousness. Regardless of the preferred approach, even if it ...