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The Next Big Thing in Artificial Intelligence is Student Assessment

For many years, the assessment of student learning has been a manual and time-consuming process. From handing out quizzes on paper to grading essays by hand, teachers have always had to be present and on-site for assessing their students. But now with the advent of artificial intelligence in education, we are seeing more and more schools use digital technologies as a means for assessing student learning. 

As technology advances so do our understanding of what it takes to create assessments that not only measure knowledge but also promote critical thinking skills such as creativity or problem-solving abilities. 

This blog post will explore how artificial intelligence is being used in various ways throughout education from scoring exams to providing feedback during lectures.

 

What is Artificial Intelligence or AI?

 

Artificial Intelligence (AI) can be defined as the science and engineering of making intelligent machines, especially intelligent computer programs. AI is used in all those processes which can be performed by thinking or cognitive process. It enables a device or a computer program to perform tasks that would otherwise require human intelligence. 

Applications of AI are found in several different fields including robotics, language processing, data mining, etc. It has been influenced by knowledge in different fields like computer science, mathematics, psychology, linguistics, etc.

Artificial Intelligence is used in higher education in different ways, such as:

  1. Analyze education data: AI can provide insights to educators and administrators at all levels. This will also provide real-time feedback to teachers based on personalized learning systems.
  2. Easy grading for teachers: If the machine has already read the essays, it will be much faster for it to give grades.
  3. Extensive personalization of educational content: It can help students learn more deeply by engaging with materials that have been tailored for them. Artificial intelligence can provide such personalization in the course material, learning environment, and tutoring systems. 
  4. Personalized feedback to students: Artificial Intelligence is helping teachers assess their strengths and weaknesses. Some systems even measure student sentiment by extracting information from online conversations about the course material.
  5. Use of digital assistants to personalize learning: The teachers can direct their students based on individual needs and skills. Such capabilities are present in the new learning management system of Moodle 3.0.

 

5 Artificial Intelligence Technologies Used in Evaluation of Students 

 

Artificial Intelligence (AI) is increasingly being used in grading examinations too, but not all people are aware of this. The following paragraphs will highlight five artificial intelligence systems used in assessing student performance:

  • Optical Mark Reader

This system is designed to recognize and read text, marks, or symbols that are either handwritten or printed on paper. The Optical Mark Reader (OMR) system is one of the most common technologies used for automated grading of examinations. This technology is currently being applied in different parts of the world including the United States, India, Japan, South Korea, and Australia.

  • Intelligent Essay Assessment

This is an artificial intelligence-based system that assesses students’ essays and provides them with scores in terms of different factors such as grammar, vocabulary, and logical structure. Intelligent Essay Assessor (IEA) is a web-based computer program that can grade students’ written assignments accurately without human interference.

  • Intelligent Tutoring Systems

The Intelligent Tutoring System (ITS) is another example of artificial intelligence technologies used in assessing students. This technology provides personalized instruction to learners based on their performance and ability. ITS can provide immediate feedback to students, respond to learner failure or success, adjust educational pace, and adapt teaching style to the diversity of student backgrounds.

  • Online Video Annotation and Summarization

This type of system can analyze students’ mistakes or errors in a video by tracking their eye movements during viewing the clip. The online Video Annotation and Summarization (VAS) system can record, store and summarize data from multiple-choice examinations based on eye movement recordings of students watching video clips.

  • Online Visual Analytics for Intelligence Data

This is an artificial intelligence-based system that assesses students’ exam papers by visualizing the data collected from the field of education. This technology provides a quick assessment of student performance through representing big data in graphs and charts using interactive technologies such as the web, smartphones, and tablets.

 

Pros and Cons of Using Artificial Intelligence in Student Assessment

 

Proponents for using artificial intelligence in assessment argue that it will:

  • eliminate time-consuming procedures, 
  • make assessments more efficient, and 
  • provide many educational benefits including the sorting out of qualified from unqualified students.

However, opponents state that any type of assessment cannot be automated as it requires creativity and flexibility. In an article published in the “Times Higher Education” journal this June, Margaret Boden argues that ‘one needs an ability to make judgments and decisions – to exercise creativity and judgment – which one cannot program computers to exhibit’.

They also argue that artificial intelligence undermines the personal touch and does not provide the convenience of a human interaction required for effective feedback.

The good thing about using AI in student assessment is that we can automate time-consuming procedures like:

  • checking for plagiarism,
  • having a computerized corrector that reduces human bias and error, and
  • strengthening the criterion-related validity of examinations, which ensures that the actual criteria being tested are what is actually being assessed.

A study conducted by the University of Connecticut in 2010 revealed that providing students with immediate feedback on their test results helped them improve their correct answers (based on long-term memory retention tasks) from approximately 50% to almost 90%.

Institutions that use artificial intelligence in assessing students can:

  • save resources by alleviating the need to hire additional staff to grade student assignments,
  • provide an increased number of opportunities for students through the availability of idealized courses, and 
  • increase satisfaction among both teachers and students.

 

Behavior Models used in Artificial Intelligence Student Assessment Systems  

 

Various types of behavior models are used in advanced Artificial Intelligence systems to investigate human-like learning behavior. These include:

Natural Language Processing or NLP Method

Natural language processing methods are becoming popular because they provide the opportunity for all types of users to communicate with computers. The researcher says that these intelligent assistants can guide students through their coursework, answer queries, find information and provide a friendly interface to IT systems.

These artificial intelligence programs process language as well as communicate with other computer applications and make them more efficient. They work faster than humans and help in saving time, money, and efforts of people since they can be used everywhere such as hospitals, etc., instead of just computer labs.

Cognitive Model

The cognitive model provides information about how to use:

  • positive feedback, 
  • negative feedback, and 
  • corrective feedback for better results. 

It also includes the different types of activities that should be carried out to assess the behavior of students i.e., whether it is correct or incorrect. This model will be helpful for both students and teachers.

Motivation Model

The motivation model provides information about the different types of motivation, self-efficacy, etc., which are required to achieve good results. 

Personal Model

Personal models provide information about the behavior of each student which can be used by teachers to assess their learning patterns. These models are helpful not only for the teachers and students but also for educational institutions as well as companies. For example: 

  1. There is a need to predict the learning patterns of students in educational institutions with the help of different types of behavior models. This will provide more accurate results and save time, effort, and money of people. 
  2. These models can be used to improve the overall performance of students. 
  3. This kind of system can be applied in other fields or domains also which will prove to be beneficial for people.

These behavior models are helpful not only for teachers but also for students by providing them with different types of training and guidance for their courses, coursework, and other activities.

 

AI-based Tests and Exams to Improve Learning Outcomes of Students 

 

AI-based coursework and assessments provide more tailored, efficient, and effective learning opportunities, such as:

  • Real-time data analysis and feedback: AI used in online assessments comments on students’ answers, and allows them to adjust their submission before receiving grades. It may also track how many times a question was answered incorrectly and use that information as a predictor for success.
  • Itemized progress monitoring: Intelligent tutoring systems can identify misconceptions, monitor students’ progress topic-by-topic, and provide personalized learning options for students based on concepts they do not understand. The automated reports they generate suggest areas where improvements need to be made before the next lesson is taught.
  • Facilitate pre-course planning: AI can be used to predict student performance. It allows faculty to better plan which students should enroll in their courses. This includes online and hybrid courses offered to large classes. Artificial intelligence can help teachers experiment with what works best for different types of students. 
  • Quick scoring option: The use of AI in teaching leads to increased efficiency when scoring assessments and providing feedback. This is especially true for large and small assessment projects and experiments, such as research and lab reports, term papers, and project proposals.

 

How does the use of Artificial Intelligence in Student Evaluation change Education? 

 

The use of AI in the field of education is leading us to:

  • More personalized learning experiences,
  • Deeper learning experiences,
  • Greater access to education for all students, including those who are struggling to afford the current cost of education,
  • Improved educational outcomes across all types of learners,
  • Enhanced reporting for students’ strengths and areas that need improvement,
  • Increased satisfaction by faculty and students,
  • Faculty workload reduction,
  • Changes in the way that instructors design their courses for maximum learning outcomes, taking into consideration student diversity and increased pace of technology change,
  • Better use of existing resources through AI-based automation, and
  • Improvements in student outcomes, including retention rates and timely degree completion. 

We, at Transtutors, love to embrace Artificial Intelligence and other leading technologies and use them for the benefit of our students.

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September 27, 2021

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