One focus for the development and deployment of artificial intelligence (AI) in education is the assessment of students.
Significant investments have been made in AI assessment tools and development, with US$166 million invested in 2022 alone. More than 15% of EdTech companies are focused on assessment.
There are seven major developments worth following.
1. Assessment at scale
AI has been used to support student learning and assessment for many years. Using papers first graded by instructors, AI grading systems learn how assessment rubrics are applied and then apply these rubrics at scale. Just 15 marked papers can lead an AI system to assess 10,000 papers accurately. AI systems can do this for a variety of forms of assessments, from mathematical problem solving to short or long essays and multiple choice.
These tools can also assess the same assignment in a variety of languages without being biased by external factors such as handwriting, culture or the language the student chose.
Although there are many such systems, the challenge for educators is affordability, training in the use of these systems and the time required to train the system to grade appropriately.
2. Automated item generation
One challenge for instructors is to design an assessment instrument, whether it be a formative assessment intended to aid learning or a summative assessment intended to provide a grade for learning.
AI can generate items used in tests, examinations and assessments. The instructor creates an item model, showing the system what an ideal test or set of tests looks like. The system then generates samples, which can be refined. For example, if an assessment typically has 10 items — four on key knowledge, three testing the knowledge in use and three on alternative approaches to problem-solving — the AI system replicates this approach in each sample test it creates. Once the AI “samples” are confirmed or modified by the instructor, the AI system generates thousands of versions of the test.
Because of the volume of equivalent tests created, students can move from periodic assessments to assessments on demand. Rather than write an exam on a specific date, they can ask to be tested at any time. Their test differs from others in terms of the specific items, but the knowledge and understanding under review are identical in every case.
3. AI and peer-to-peer assessment
One aspect of assessment practices that has grown significantly in the past decade is the use of software to support peer-to-peer assessment. Services like Kritic, Peerceptiv® and Teammates all make use of smart technologies and AI to facilitate fair and appropriate peer assessment.
They do so by:
- Offering guidelines and assistance to student assessors during individual reviews to help them provide better feedback to fellow students.
- Integrating probabilistic and text analysis inference models to improve the accuracy of the assigned grades and removing bias and “trade-off” deals between students.
- Developing feedback on review strategies that enable peers to assess and review the work of each other as assessors.
- Employing a spot-checking mechanism to help instructors optimally oversee the peer assessment process to promote consistency across a group of assessors.
4. AI and project-based learning and assessment
Effective project-based learning is focused on demonstrating learning in action — applying knowledge, capabilities and skills to a project and using that experience to enhance and enrich learning.
Ideally, knowledge, skills and capabilities should be assessed before the project starts, during the project (several times) and at the end so learning gains can be mapped. AI can be used to generate appropriate assessment tools.
AI products like ChatGPT or other forms of chatbot are used to support the students' experience. The tools provide suggestions on how to complete a task, locate skills development videos or tools and provide resources that can support project work 24/7.
AI-supported systems like Valid-8 support the assessment of competencies, demonstrated by evidence submitted in a variety of formats: video, audio or text or some combination of these. By using AI to automate cross-referencing to competency statements and task-based evidence, Valid-8 can accelerate the completion of legally defensible competency tasks.
5. Supporting inclusion
There is a significant opportunity for AI-enabled supports to help learners with exceptionalities.
- Partially sighted learners can convert text to speech and speech to text, enabling them to undertake assessments created for sighted persons.
- Text or audio can be translated for learners whose first language is not the same as the language of instruction. An assessment can be translated instantly from one language to another, and responses written in their own language can be assessed as if written in the language of instruction.
- Video captioning, generated automatically, can enable a deaf or person with limited hearing to fully understand a Zoom session or face-to-face interaction.
- For students with speech impediments, Voiceitt uses machine learning to pick up speakers’ unique speech patterns, recognizes any mispronunciations, and normalizes speech before creating an output of audio or text.
- Students who use sign language can now use an AI-supported skill on Amazon Alexa, which converts their signing to speech, enabling them to be understood by those who do not have signing skills.
- There are many developments related to students with exceptionalities. Many people in the AI development community are committed to equity and inclusion and are working hard to enable the full engagement of these students in their learning.
6. Checking for cheating
As AI reviews a student's work, it can also automatically check for plagiarism and other forms of cheating (e.g. two or more students in the same cohort providing identical answers). AI systems can also compare handwriting samples from students’ past work with their examination submission to verify that the student who took the exam is the same person who submitted assignments during the course.
AI proctoring systems such as Examonline, ProctorEdu or Examroom can also use biometrics — facial recognition and fingerprint recognition — as well as writing forensics to determine whether the student writing an exam is in fact the person they claim to be. Such systems also monitor activity in the space the student is using to make sure they are not cheating.
7. What’s next?
Given the growth of the AI-enabled tools described, we can expect to see more assessment-only credentials, such as those offered by the University of Wisconsin, more deployment of assessment on demand and more use of competency-based assessment.
Although some faculty and instructors are more concerned about academic misconduct, others will use the emergence of highly functioning AI systems to change and improve how students are assessed.
