An empirical investigation of student application behavior in HVAC learning aided by artificial intelligence (AI)
Authors: Khoa Bui Quoc , Nguyen Hoang-Tien , Anh Dinh Ba Hung , Khanh Nguyen Hoai , Toai Nguyen Van 
Published: February 2025

About 40% of the energy used in buildings is used for heating, ventilation, and air conditioning (HVAC). As a result, it has emerged as a key area for reduction in both energy use and CO2 emissions. Traditional approaches to HVAC optimization, control, and fault diagnosis will find it difficult to satisfy crucial needs like energy efficiency, occupancy comfort, and dependable fault detection in the context of advancements in building intelligence and energy technologies. In this sense, machine learning and data science have enormous promise, especially in light of advancements in sensor technology and information technology that make vast amounts of high-quality data accessible. However, a number of obstacles need to be overcome before machine learning can be widely used in business. The recent research on machine learning for HVAC system optimization, control, and problem detection is compiled in this review. In contrast to other reviews, we offer thorough analysis of the applications, taking into account all relevant factors. Following a succinct introduction to machine learning and its applications to HVAC, we critically evaluate the most recent research on control, optimization, and fault diagnosis and detection. Lastly, we offer a thorough analysis of the shortcomings of the present research and make recommendations for future lines of inquiry.
Artificial intelligence is becoming more and more popular in many facets of life, especially education, in the aftermath of the fourth industrial revolution. With an emphasis on students at Ho Chi Minh City University of Van Lang, this study examines the variables influencing how students use artificial intelligence (AI) in their education. Subjective norms, image, job relevance, output quality, result demonstrability, self-efficacy, anxiety, perceived playfulness, perceived enjoyment, perceived ease of use, perceived usefulness, and behavioral intention are all examined in this study using a combination of the technology acceptance model and the theory of planned behavior. Combining various technological models offers fresh perspectives on AI, which can either help or hurt user behavior due to bias. The survey was completed by 390 students using the stratified sample technique, and the findings were then examined using the least squares linear structural model. The most important criteria impacting students' inclination to employ AI in learning, according to the study, are perceived usefulness and ease of usage. Students' perceptions and plans to employ AI are significantly shaped by subjective norms as well. The study also emphasizes the significance of playfulness, output quality, outcome demonstrability, self-efficacy, perceived enjoyment, and work relevance in shaping students' attitudes toward the usage of AI. The results of this study emphasize how important it is for educational establishments to foster an atmosphere that supports students' use of AI in the classroom. On the other hand, in order to make AI tools more accessible and user-friendly, developers of AI technology must concentrate on streamlining the user interface. These useful suggestions from the study help direct choices about educational technology policy and design. Lastly, rather than drawing a conclusion, the study also seeks to expand the ways in which academic AI systems can be used.