Enhancing Learning With AI-Powered Assessments

5 déc. 2024

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Generative Artificial Intelligence has been integrated to almost all digital learning platforms to provide new services, such as the creation of eLearning courses, videos, text-to-speech etc. But what about assessment?

Enhancing Learning With AI-Powered Assessments

A New Wave In Online Learning: AI-Powered Assessments

Assessments are key to creating truly efficient learning experiences that address each learner's needs. Quizzes and questionnaires can be used in various ways and incorporated at different points in the learning process to stimulate growth. For instance, short quizzes can be inserted between sections of an eLearning course to engage learners. These assessments are not exams and are not conducted under controlled conditions, but they are absolutely essential. They encourage learners to actively compare what they heard or read in the course with their actual understanding. Each question provides explanations and insights to help learners grasp concepts and refine what they have learned.

Assessments can also be conducted before a training course begins to gauge the initial knowledge levels of participants. Learners are then assessed under controlled conditions at the end of the course. By comparing the results of these two assessments, educators can precisely measure the knowledge acquired during the course. This comparison offers valuable insights into the course's quality and highlights areas that may need improvement.

Digital assessments are also central to implementing truly effective adaptive learning approaches, focusing on individual weaknesses and placing each student at the center of the learning process.

Assessment can take various forms, from short, interactive quizzes to controlled exams that determine whether a learner earns a certification or diploma, potentially impacting their career. Assessment can also include surveys completed by learners, trainers, or managers to evaluate the quality and operational impact of a training course.

While online assessments are undeniably effective and essential, they can often be challenging to implement. Creating questions and questionnaires takes time and must be seamlessly integrated into each learning course to create unique and engaging learning experiences.

How AI Can Revolutionize Assessment And Transform Learning

Artificial Intelligence is a powerful tool that offers many opportunities, including facilitating the use of online assessment.

1. Automating Question And Questionnaire Creation

Creating questions and questionnaires can be time-consuming. Each question requires at least one correct answer and several incorrect options, depending on the question type. High-quality questionnaires should include diverse question formats, such as multiple-choice, open-ended, and matching questions, rather than just yes/no or true/false.

For quizzes embedded in eLearning courses to support learning, questions should also provide explanations to help learners understand the underlying concepts. For exams or assessments aimed at verifying knowledge acquisition, more extensive question sets are required for reliability. A good assessment should include at least 30 questions.

Generative AI can significantly speed up question creation:

  • Using prompts
    By providing clear instructions about the desired topics, AI can generate a list of questions with correct and incorrect answers. These questions can be directly integrated into quizzes and assessments. This method is ideal for general knowledge topics that are well-documented online. AI can also provide explanations with references to help learners understand the answers.
  • Using specific documents
    For specialized topics, such as a company's internal policies or health and safety guidelines, AI can generate precise questions based on provided documents. This method is ideal for employee onboarding, customer training, or other niche areas.
  • Generating distractors
    In cases where questions and correct answers are predefined, creating plausible incorrect answers (distractors) can be tedious. AI can assist by generating distractors, saving valuable time.

As always, AI-generated content must be reviewed to ensure accuracy and eliminate redundant or incorrect information.

2. Automating Open-Ended Questions Grading

Closed-ended questions, such as multiple-choice, true/false, and matching questions, can be easily graded automatically. Open-ended questions, however, pose a greater challenge. These require manual grading, as each response must be reviewed, corrected, and graded individually—a time-intensive process. Consequently, trainers may hesitate to use this valuable question type.

Open-ended questions are highly beneficial, promoting critical thinking and a deeper understanding of the subject matter. They provide insights into a learner's thought process and allow educators to gauge not just what a student knows, but how they think, approach problems, and formulate arguments. This is particularly valuable in identifying areas where a learner may need support or further development. They encourage learners to explore ideas, analyze concepts, and express their thoughts comprehensively, fostering creativity, analytical skills, and independent thinking.

AI can simplify the grading process for open-ended questions. With proper integration into digital learning platforms, AI can analyze responses, provide feedback, and assign grades. Additionally, AI can generate sample corrections for each response. Trainers can then focus on poorly graded answers and AI-generated comments, significantly speeding up the grading process.

3. Analyzing Survey Responses

Assessments can also take the form of surveys, gathering feedback on a training course. These surveys often include open-ended questions where learners can freely share thoughts about the course.

Analyzing these responses manually is time-consuming but essential for gaining a comprehensive understanding of learner satisfaction and identifying areas for improvement. AI can assist by analyzing text responses and assigning sentiment scores. For example, a highly dissatisfied response might receive a score of 1, while a highly positive response might score a 10. Analysts can then focus on low-scoring responses, streamlining the feedback review process.

First published on elearningindustry.com.

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