It was a cold winter morning, and the students in Mr. Johnson's classroom were anxiously waiting for their graded assignments. Mr. Johnson had spent countless hours after school grading stacks of papers, and the students were nervous about what they would receive. Finally, the day before everyone went on break, Mr. Johnson handed back the graded papers, one-by-one. As the last paper was returned, Mr. Johnson let out a sigh of relief. He knew that grading was a tedious and time-consuming task, and he wished there was a better way. Little did he know, the answer to his problem was just around the corner, in the form of artificial intelligence.
In this blog post, we will discover:
The different ways in which AI is being used in grading and assessment.
The benefits and drawbacks of using AI.
How to save time on grading & assessment in your classroom.
"AI is helping to automate some of the more routine grading tasks, such as multiple-choice questions, freeing up time for teachers to focus on more impactful work like personalized teaching and mentorship." - Andrew Ng, Co-Founder of Coursera
Used to provide feedback, measure student learning outcomes, and establish a direction for curriculum development, grading, and assessment are an essential part of education. However, the traditional methods of grading and assessment are often time-consuming, subjective, and prone to errors. The emergence of artificial intelligence (AI) has the potential to revolutionize the way we grade and assess students. AI can provide more objective and efficient evaluations, allowing educators to spend more time on teaching and personalized learning.
The Types of AI Grading and Assessment
AI can be used to grade and assess different types of assignments, such as essays, short-answer questions, and collaborative problem-solving. The use of AI varies depending on the type of assignment and the context in which it is used.
For essays, AI can be used for both holistic and analytical scoring. Holistic scoring looks at the overall quality of the essay, while analytical scoring assesses specific aspects such as grammar, syntax, and vocabulary. AI can use different techniques such as rule-based, statistical, and machine learning approaches to score essays. For instance, machine learning models can be trained on a large dataset of essays to recognize patterns and generate scores based on those patterns. However, there are limitations to AI essay scoring, such as the inability to assess creativity and critical thinking.
Short-answer questions in STEM education can also be graded using AI. AI can use different techniques such as keyword matching, machine learning, and natural language processing to grade short-answer questions. However, the effectiveness of AI grading depends on the complexity and structure of the questions. AI can also be used to assess collaborative problem solving, using approaches such as analyzing chat logs, tracking mouse movements, and analyzing audio and video recordings.
The Benefits and Drawbacks of AI Grading and Assessment
The use of AI in grading and assessment offers several benefits. One of the primary benefits is the potential to provide more objective and consistent evaluations. Unlike human graders, AI is not subject to bias, fatigue, or human error. This can lead to more accurate and fair evaluations. AI can also provide faster feedback to students, allowing them to make improvements and learn from their mistakes in a timely manner. Additionally, AI can help educators save time and resources, as grading can be a time-consuming task. This can allow educators to focus on other aspects of teaching, such as personalized learning and providing individualized attention to students.
"By using AI to automate grading tasks, teachers can spend more time on lesson planning, student engagement, and other areas that require human input and creativity." - Fei-Fei Li, Co-Director of the Stanford Institute for Human-Centered Artificial Intelligence
Artificial intelligence also has many opportunities for educators to leverage AI grading and assessment in innovative ways. For instance, AI can be used to provide personalized learning recommendations based on students' performance data. AI can also be used to identify patterns in student performance data, allowing educators to make data-driven decisions about teaching and curriculum development. Furthermore, the use of AI grading can free up educators' time and resources, allowing them to focus on other important aspects of teaching.
What the Research Says
Several recent studies have explored the potential benefits and challenges of using AI in educational grading and assessment.
One report suggests that incorporating artificial intelligence in grading and assessment can free up 13 hours a week for teachers, allowing them to focus on creative and collaborative activities that enhance the learning experience for students. AI-powered grading and assessment can also provide more personalized feedback to students and help identify areas where they need additional support.
The article "Artificial Intelligence in Education: A Review" by L. Chen, P. Chen, and Z. Lin provides an overview of the current state of AI in education. The article covers a wide range of topics such as intelligent tutoring systems, personalized learning, educational data mining, and natural language processing. The authors highlight the potential benefits of AI in education, such as improving student engagement and motivation, providing personalized feedback, and facilitating teacher decision-making.
In their 2021 article "Artificial Intelligence for Student Assessment: A Systematic Review," González-Calatayud, Prendes-Espinosa, and Roig-Vila conducted a comprehensive review of the literature on the use of AI in student assessment. The authors analyzed 40 articles that met their inclusion criteria and found that AI can effectively evaluate students' knowledge and skills in various domains, including language, math, and science. They also noted that AI can provide valuable formative feedback to students and teachers, help reduce bias in grading, and enable more personalized and adaptive learning experiences. However, the authors caution that further research is needed to better understand the ethical and practical implications of using AI in education and to ensure that AI-based assessments are valid, reliable, and fair.
However, the use of AI in grading and assessment also has its drawbacks. One of the main concerns is the potential for AI to replicate or reinforce existing biases in the data it is trained on. This can lead to unfair evaluations and perpetuate inequality. Additionally, AI grading may not be able to capture the nuances of certain types of assignments, such as essays that require creativity and critical thinking. There is also a concern that AI grading may reduce the value of human feedback and interaction in the learning process.
Implementing Artificial Intelligence Grading & Assessment in Your Classroom
There are several different types of AI grading and assessment tools that can be used in the classroom. Here are some examples:
Automated Multiple Choice Question Grading
This type of tool uses machine learning algorithms to automatically grade multiple-choice questions. It can help save time for educators by quickly grading large volumes of assignments and providing instant feedback to students.
Gradescope: This platform uses AI to grade multiple choice questions, as well as short answer and programming questions.
ZipGrade: This platform uses AI to grade multiple choice questions, and also allows educators to create and print their own answer sheets.
Remark Test Grading Cloud: This platform uses AI to grade multiple choice questions, and also offers a variety of reporting and analysis features.
Flubaroo - an add-on for Google Sheets that grades multiple-choice questions and provides instant feedback to students.
QuickKey - a mobile app that allows teachers to scan and grade multiple-choice assessments with their smartphone or tablet.
ExamSoft - a secure assessment software that uses artificial intelligence to grade multiple-choice questions and provide analytics to improve student outcomes.
Automated Essay Scoring
This tool uses natural language processing (NLP) algorithms to analyze written essays and provide a score. It can help educators save time grading essays while still providing consistent and objective evaluations. (Read more about NLP in From Legos to Cats: How We Talk with Artificial Intelligence)
Turnitin: This platform uses NLP algorithms to analyze written essays and provide a score, as well as detect instances of plagiarism.
EdX: This platform uses AI to grade essays and provide feedback, and also offers personalized learning recommendations.
MI Write: This platform uses AI to analyze writing skills and provide feedback on grammar, syntax, and more.
ETS e-rater: Developed by Educational Testing Service (ETS), e-rater is a natural language processing tool that provides automated scoring for essay-based assessments.
MY Access!: MY Access! uses a holistic scoring algorithm to evaluate essays based on multiple writing traits, such as organization, idea development, and language use.
Essay Grader: This tool is designed to help teachers quickly and easily grade essays, with features like customizable rubrics and the ability to provide feedback and comments to students.
Criterion Online Writing Evaluation: Criterion Online Writing Evaluation is an automated essay scoring tool that provides instant feedback on essays, including feedback on grammar, usage, and mechanics.
PEG Writing: PEG Writing is an AI-powered essay scoring tool that provides instant feedback and helps students develop writing skills through its interactive platform.
Speech and Language Recognition
This tool uses NLP and speech recognition technologies to analyze spoken language and provide feedback on pronunciation, grammar, and syntax. It can be used to assess language proficiency in foreign language classes, for example.
Rosetta Stone: This platform uses speech recognition technology to analyze pronunciation and provide feedback on language proficiency.
Duolingo: This platform uses speech recognition technology to assess pronunciation and grammar in language learning exercises.
Lingoda: This platform uses videoconferencing and speech recognition technology to provide live language classes and personalized feedback.
Google Cloud Speech-to-Text: A tool that transcribes speech into text with high accuracy.
Amazon Transcribe: A machine learning-based tool that transcribes audio files into text in multiple languages.
Microsoft Azure Speech Services: A tool that can recognize and transcribe speech in real time, with customization options available.
IBM Watson Speech to Text: A tool that can transcribe speech into text in multiple languages, with customizable vocabulary and punctuation options.
Dragon NaturallySpeaking: A tool that can transcribe speech into text with high accuracy and allows for voice commands to control the computer.
Adaptive Learning Platforms
These platforms use AI algorithms to analyze student performance data and provide personalized learning recommendations. They can adapt to each student's unique learning style and pace, providing targeted support to help them succeed.
Knewton: This platform uses AI to analyze student performance data and provide personalized learning recommendations for K-12 and higher education.
Smart Sparrow: This platform uses AI to create adaptive learning experiences for students, and also offers tools for educators to create their own adaptive courses.
DreamBox Learning: This platform uses AI to provide personalized math learning for K-8 students, adapting to each student's unique learning needs.
Aleks: An adaptive learning platform that provides personalized math and science instruction, with a focus on K-12 education and college readiness.
McGraw Hill Connect: An adaptive learning platform that provides personalized content and assessments for a wide range of subjects, including business, science, and language arts.
This type of tool uses machine learning algorithms to scan student assignments and detect instances of plagiarism. It can help educators ensure academic integrity and teach students about the importance of citing sources and avoiding plagiarism.
Turnitin: In addition to AES, Turnitin also offers a plagiarism detection feature that uses AI to scan student assignments for instances of plagiarism.
Unicheck: This platform uses AI to detect instances of plagiarism in student assignments, as well as provide feedback on grammar and spelling errors.
Grammarly: This platform uses AI to detect instances of plagiarism, as well as provide feedback on grammar, syntax, and other writing issues.
Copyscape - A web-based plagiarism detection tool that compares submitted work to other online sources and previously submitted work.
PlagScan - A plagiarism detection tool that compares submitted work to online sources and previously submitted work, and provides a detailed report of similarities.
Urkund - A cloud-based plagiarism detection tool that compares submitted work to online sources and previously submitted work, and provides a detailed report of similarities.
These are just a few examples of the types of AI grading and assessment tools that are available to educators. It's important to choose the tool that best fits your teaching context and goals and to be aware of any potential biases or limitations of the tool. My recommendation for choosing the technology tool that best fits your classroom is to use The Triple E Framework.
Artificial Intelligence has the potential to revolutionize the way we grade and assess students. By automating routine grading tasks, AI frees up teachers' time to focus on more impactful work such as personalized teaching and mentorship. However, the use of AI grading and assessment also has its challenges and drawbacks, such as the potential for bias, and the limitations of the technology in certain types of assignments.
"The key to making AI work for education is to focus on areas where it can augment and enhance the work of teachers, rather than simply replacing them." - Andrew Ng, Co-Founder of Coursera
An important part of successfully implementing AI grading and assessment is to focus on areas where it can augment and enhance the work of teachers, rather than replace them. Teachers must also be aware of the potential biases in the data used to train the AI models and must ensure that AI grading does not replace human feedback and interaction. By leveraging the benefits of AI grading and assessment while addressing its limitations, educators can create a more objective, efficient, and personalized learning experience for their students.
González-Calatayud, V., Prendes-Espinosa, P., & Roig-Vila, R. (2021). Artificial Intelligence for Student Assessment: A Systematic Review. Applied Sciences, 11(12), 5467. https://doi.org/10.3390/app11125467
Klein, A. (2020, January 29). Artificial intelligence could free up 13 hours a week for teachers, report finds. Education Week. Retrieved from https://www.edweek.org/technology/artificial-intelligence-could-free-up-13-hours-a-week-for-teachers-report-finds/2020/01
L. Chen, P. Chen and Z. Lin, "Artificial Intelligence in Education: A Review," in IEEE Access, vol. 8, pp. 75264-75278, 2020, doi: 10.1109/ACCESS.2020.2988510.