Artificial Intelligence (AI) has emerged as a transformative force across various industries, and its potential in the field of education is increasingly being recognised. To me, this is an exciting time, and an opportunity for educators and learners to harness the power of AI in education; it has the power to revolutionise teaching and learning processes. In this short article, I would like to briefly explore the potential of AI in education, including its applications, benefits and some possible challenges.
AI can personalise the learning experience for students by analysing their individual strengths, weaknesses, and learning styles. Intelligent tutoring systems can provide tailored content, adaptive assessments, and real-time feedback. For instance, Duolingo and Khan Academy makes use of AI algorithms to deliver personalised language learning and maths/science education respectively, based on learners’ progress and performance. Research shows that personalised learning improves student engagement, motivation and outcomes (D’Mello & Graesser, 2012). Outside classrooms, platforms such as these can make a positive difference to the learning of the users. Admittedly, that is also dependent on how well designed the platform is and perhaps the frequency of usage.
Intelligent Student Support
AI-powered chatbots and virtual assistants can provide round-the-clock support to students, answering their queries and assisting with administrative tasks. These virtual agents can help in guiding students through online courses, providing study resources, and addressing common questions.
I watched a TedTalk (by Ashok Goel) about Georgia Institute of Technology’s “Jill Watson” chatbot, which is a notable example that responded to student inquiries in an online course without revealing its AI nature! Professor Goel’s project aimed to explore the potential of AI in providing support to students and alleviating the workload of educators/teachers. Such systems reduce the burden on teachers while providing timely and personalised support to students (Martinez-Maldonado et al., 2019). The introduction of Jill Watson had a positive impact on student engagement and satisfaction. It reduced the response time for queries, providing students with immediate feedback and support, even during non-working hours. The chatbot’s ability to handle routine administrative tasks and FAQs allowed human instructors to focus on more complex interactions and personalised teaching. I have noticed that chatbots are being more commonly used now, even outside education (e.g. online banking and logistics companies), but at the moment, there is still a lot of room for refinement.
Intelligent Content Creation
AI-powered tools can assist teachers in developing high-quality educational content/resources. For instance, quizzes, lessons plans, teaching resources can be automatically generated; even if modifications need to be made, time and effort required by educators can be reduced. As our in-house AI Team has recently demonstrated in INSET, AI such as ChatGPT can be used in a variety of ways. So far, I have tried using ChatGPT to generate some questions for a mini quiz as a starter activity. It does depend on the user’s input into ChatGPT, but I have had fun experimenting with it, and I have made some useful resources. AI can also analyse vast amounts of data to identify knowledge gaps and suggest appropriate content for students. By leveraging AI, teachers can focus more on instructional design and personalised interactions with students.
Automated Grading and Assessment
AI offers the potential for automated grading and assessment, saving valuable time for teachers and providing faster feedback to students. Machine learning algorithms can evaluate objective assessments, such as multiple-choice questions, with high accuracy. This enables educators to allocate more time to tasks that require human judgment, such as grading long answers/essays and providing qualitative feedback. Research indicates that automated grading can lead to increased consistency and efficiency in assessment processes (Messer, 2022).
In the Science Department, we use a platform called Educake. This is an example where AI saves time: it is very easy and quick to set questions (they are provided by the platform but the teacher selects the questions to be included in the quiz), and as the student completes each question (normally short answer or multiple choice), answers are marked automatically with feedback. If a student disputes the answers, the teacher can then review and give further feedback. If used in class, the teacher can see in real-time the progress of students in the class (see Figure 1), and if there is a question that many find challenging, the teacher can address it.
Data-Driven Decision Making
Al can provide valuable insights to educators, helping us make informed decisions. By analysing student performance, and engagement levels, AI systems can identify at-risk students, recommend interventions, and optimise teaching strategies. Predicative analytics can assist in early intervention and targeted support, ultimately improving performance (Krishnamoorthy et al., 2022).
In the Chemistry Department, we encourage our Sixth Form students to use UpLearn, where they can watch a series of short videos for a specific topic, and they need to answer some questions to assess their understanding. The more they do, the more XPs they will achieve. In my opinion, this is a useful tool, as we can encourage independent study outside lessons, and we can track how long they have spent on the platform and how many XPs they have achieved (see Figure 2); whether they are actively making use of the platform by answering questions or not. If the student does not answer the questions very well, UpLearn will suggest videos for the student to watch in order to address the weakness.
In summary, my thoughts are that this is an exciting time where technologies are developing very fast. We have already seen the benefits and the potential of AI in education and potential drawbacks or dangers. I wish to continue to explore the use of AI in teaching and learning.
At the time of writing this, Apple has just announced the launch of the Apple Vision Pro headset, which promises to free the students from the restriction of 2D screens. I expect that there will be other similar devices being introduced in the near future.
It will be interesting to see how education will change; I think it is important to address ethical concerns, ensure proper training for educators, and maintain a balance between technology and human interaction to fully harness the potential of AI in the education landscape.
Ashok Goel’s TedTalk: A teaching assistant named Jill Watson | Ashok Goel | TEDxSanFrancisco – YouTube
D’Mello, S., & Graesser, A. (2012). Dynamics of Affective States during Complex Learning. Learning and Instruction, 22, 145-157.
Martinez-Maldonado, R., Hernández-Leo, D., & Pardo, A. (2019). Preface to the special issue on learning analytics and personalised support across spaces. User Modeling and UserAdapted Interaction, 751-758.
Messer, M. (2022). Grading Programming Assignments with an Automated Grading and Feedback Assistant. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham.
Krishnamoorthy, S & Soumya MD. (2022). Student performance prediction, risk analysis, and feedback based on context-bound cognitive skill scores. Educ Inf Technol 27, 3981–4005.
‘AI and Teaching: The Future is Now’ by Sandy Clarke, published in The Enquiry: Issue 6.
The Enquiry is a staff journal dedicated to reflections on educational research, and teaching and learning at Downe House School. Issue 6 was published in November 2023, looking back at Summer term 2023.
All previous issues can be found here: The Enquiry by downehouseschool Stack – Issuu.