Hello everyone dear FounderN readers, in this week’s blog column, we will take a quick look at the answers to questions such as what steps have been taken in the field of artificial intelligence education as we have just finished 2024, in which areas have we made progress, which areas have we revised, etc. We hope this is an article that you will be happy with, and have a pleasant reading!
A) Personalized Learning Systems
Personalized learning is one of the most important details in artificial intelligence. This system identifies the student’s learning speed, learning style, and areas of deficiency and offers them a personalized (individualized) education plan.
Let’s look at the technologies that can be given as examples of personalized learning systems:
Comprehensive Learning Management Systems (LMS): Knewton and DreamBox are some of the platforms that adapt courses based on student performance. Such system applications present learning content and teaching strategies in a personalized way for each student.
Adaptive Learning: Another concept we need to touch on in learning systems is (AL). It helps reinforce the student’s learning process by presenting new material according to the questions the student asks.
B) Automatic Evaluation and Feedback
Another point where AI helps us is the technology of automatically evaluating students’ written or multiple-choice exams . The relevant technology reduces the evaluation workload of teachers and provides rapid feedback to students.
Socrative: It is one of the platforms that provides instant feedback to students .
Turnitin: Measures the student’s level of originality by performing similarity analysis of written work and offers feedback.
C) Virtual Assistants and Chatbots
AI-based virtual assistants used in education provide students with information about lessons, assignments, exams, and more. Chatbots aim to reduce the dependency on teachers and speed up the process by answering students’ questions instantly.
IBM Watson Tutor: An artificial intelligence assistant that answers students’ questions and provides personalized lesson guidance.
Duolingo Chatbots: The Duolingo app offers language learners the opportunity to practice through virtual assistants.
D) Learning Analytics and Data Analysis
Artificial intelligence also has the service of providing information about student achievement, progress, and challenges by analyzing big data used in education . This analysis data also guides teachers in which areas they need to provide extra support to students.
Tableau: Educational institutions can use tools like Tableau to analyze student performance data and develop new strategies for student success.
Perry Systems: An application that analyzes students’ performance and provides special recommendations to teachers.
E) Automatic Content Generation
Digital intelligence is also very useful for accelerating the process of creating educational content. Teachers can use this technology to produce content specifically for students.
Quillionz: An AI tool that helps teachers prepare exam questions and midterms.
Scribe: It is an artificial intelligence-supported software that creates educational content. It helps create text and visuals to reinforce the course materials that teachers frequently prefer .
F) Teacher Support and Professional Development
Artificial intelligence also plays a role in the professional development process of teachers. It can play an important role in critical decisions such as advising teachers on new pedagogical approaches , teaching methods and technologies .
Edthena: A platform that allows teachers to record videos of their classroom performances and analyze them , allowing them to focus on areas for improvement .
TeacherKit: An application that helps teachers manage their classrooms and track their students .
G) Artificial Intelligence-Supported Student Monitoring and Intervention Systems
Artificial intelligence monitors students’ academic progress and develops early warning systems, enabling timely intervention for students at risk of failure .
Civitas Learning: Analyzes student data and offers intervention suggestions to help students achieve success.
Predictive Analytics: Uses student behavior and performance data to predict which students will need more support.
• I wanted to discuss the “Learning Analytics and Data-Based Decision Making” applications, which I think are the most important, in a little more detail.
We mentioned that learning analytics and data-based decision making offer great opportunities to make education more effective and personalized. Collecting and analyzing student data is very important to increase their success. Applications such as Moodle , Canvas and Blackboard also collect student interactions and provide teachers with a reference about student levels. The data obtained by the applications can be visualized and analyzed with programs such as Tableau and Power BI , so teachers can see where students are struggling. In addition, early warning systems can be created by determining students’ future performance with machine learning . In this way, special support can be provided for students. Tools such as Socrative and Google Classroom can be a great toolbox for these and other similar applications!
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