Machine Learning (ML) : Machine learning refers to the ability of a machine to learn and improve from experience without being explicitly programm to do so. This is achiev through algorithms that identify patterns in data and make prictions or decisions bas on them. Some applications include:
Pricting Outcomes and Trends : ML can analyze large volumes of historical data to prict future outcomes. For example, in ucation, it can prict student performance bas on their interactions with learning platforms.
Prictive Analytics : Us in university management to prict dropout rates, evaluate the effectiveness of academic programs, and optimize resource allocation.
Deep Learning (DL) : Deep Learning is a subcategory of ML that uses artificial neural networks to model and understand complex patterns. Deep neural networks consist of multiple layers that allow for more complex and abstract data processing. Its applications include:
Image Recognition : DL is us to identify and classify
Objects within images and videos. In academia, it can be appli in exam invigilation and digital library management.
Big Data Analysis : You can manage and extract valuable information from vast volumes of data, allowing for a better thailand phone number library understanding of academic and scientific trends.
Natural Language Processing (NLP) : Natural Language Processing focuses on the interaction between computers and human language. NLP enables machines to understand, interpret, and generate text in a way that is natural to humans. Some key applications are:
Text Analysis : NLP can analyze large amounts
Of text to identify themes, sentiments, and trends. This is useful for academic research and ucational content management.
Text Generation : Tools such as ucational chatbots and virtual assistants use NLP to generate coherent and useful responses in in addition integrating inbound marketing natural language, improving interaction and support for students and teachers.
These AI tools are transforming higher ucation by providing more accurate analytics, personaliz learning, and efficiency in academic and administrative management.
Benefits and challenges for teachers
AI can take over repetitive tasks such as grading exams and taking attendance, allowing teachers to focus on more meaningful cg leads pagogical activities (Owoc et al., 2021). Additionally, smart tutoring tools can help deliver personaliz ucation, adapting to individual students’ nes (Chen et al., 2020).
