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AbstractConstructive alignment is a learning design approach that emphasizes the direct alignment of the intended learning outcomes, instructional strategies, learning activities, and assessment methods to ensure students are engaged in a meaningful learning experience. This pedagogical approach provides clarity and coherence, aiding students in understanding the connection of their learning activities and assessments with the overall course objectives. This paper explores the use of constructive alignment principles in designing a graduate-level Introduction to Project Management course by leveraging Large Language Models (LLMs), specifically ChatGPT. We introduce an innovative framework that embodies an iterative process to define the course learning outcomes, learning activities and assessments, and lecture content. We show that the implemented framework in ChatGPT was adept at autonomously establishing the course's learning outcomes, delineating assessments with their respective weights, mapping learning outcomes to each assessment method, and formulating a plan for learning activities and the course's schedule. While the framework can significantly reduce the time instructors spend on initial course planning, the results demonstrate that ChatGPT often lacks the specificity and contextual awareness necessary for effective implementation in diverse classroom settings. Therefore, the role of the instructor remains crucial in customizing and finalizing the course structure. The implications of this research are vast, providing insights for educators and curriculum designers looking to infuse LLMs systems into course development without compromising effective pedagogical practices.

© 2015 Pereira, Medina, Baena, Planas and Pozas. The adult subventricular zone (SVZ) is the main neurogenic niche in normal adult brains of mice and rats. Interferon gamma (IFNγ) has somewhat controversially been associated with SVZ progenitor proliferation and neurogenesis. The in vivo involvement of IFNγ in the physiology of the adult SVZ niche is not fully understood and its intracellular mediators are unknown. Here we show that IFNγ, through activation of its canonical signal transducer and activator of transcription 1 (STAT1) pathway, acts specifically on Nestin+ progenitors by decreasing both progenitor proliferation and the number of cycling cells. In addition, IFNγ increases the number of neuroblasts generated without shifting glial fate determination. The final result is deficient recruitment of newborn neurons to the olfactory bulb (OB), indicating that IFNγ-induced stimulation of neuronal differentiation does not compensate for its antiproliferative effect. We conclude that IFNγ signaling via STAT1 in the SVZ acts dually as an antiproliferative and proneurogenic factor, and thereby regulates neurogenesis in normal adult brains. ; This work was supported by the Marató TV3, and a Mapfre foundation award to EP, and Spanish Ministry of Economy MINECO (SAF2014-56279-R) to AMP. Also EP was a researcher in the Spanish government's Ramón y Cajal program (MICINN-Spain) ; Peer Reviewed