AI & Learning: A Preferred Future

A brief summary of the article: AI & learning: A Preferred Future by Venkat Srinivasan. This is an article from a peer-reviewed journal which seeks to focus on learning applications of Artificial Intelligence in Education (AIED). 

Artificial intelligence (AI) is defined as the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from experience(1).

The paper presents chronic challenges and how AI technology has the potential to help the world address these challenges in education relating to equity, learning out-comes, and real-world relevance 

The author goes on to say that his “vision of a preferred future is manifest in a comprehensive AI-first solution framework for learning with the objectives of scaling quality education affordably, optimizing the learning potential of individual learners, enabling teachers to be more effective and parental involvement to have a positive impact.”(2)

In the article, the author introduces 3 categories of AI in learning which were modified from previous definitions. These are: Learning-oriented, Institutional operations-oriented, and policy-oriented. The focus of this paper is on learning-oriented applications in AIED(2). The author initially presents a review of research related to areas in learning with AI such as the changing role of the teacher, parental involvement the neurobiology of learning and the constructivist theory of education. He believes that a holistic view encompassing all these dimensions is critically important in arriving at a preferred future.

The paper presents different issues and challenges of adapting AI to learning. The author mentions that AI can be used in areas such as tutoring, providing personalized learning supports based on the student’s learning characteristics, and essay grading/ grading of open-text responses(2). He envisions a comprehensive solution framework. The framework approaches learning holistically and attempts to integrate foundational learning, learning science and personalization. The framework comprises several components each of which can scale specific aspects of learning irrespective of the subject.

The framework brings together all the individual components through a Learning Orchestrator personalized to the individual learner: The envisioned framework components are briefly described below:(2)

Reading Assistant  – enables read-aloud of any content with the ability for the learner to control their reading.

Comprehension Assistant  – facilitates understanding of specific concepts with learning graphs.

Automated Grader  – assesses open text response in a traceable manner providing the assessor/learner complete visibility into assessment reasoning. The grader can be configured to a course/assessment-specific rubric.

Dynamic Learning Optimized Content  – integrates new content/ knowledge with existing course content/curriculum using learning science algorithms and the human teacher’s input.

Learning Science Algorithms/Learning Optimizer – converts content to be optimized for learning reflecting learning science.

Learning Lab Certification  – educational institutions may need to create a common competency to ensure that all their offerings are properly and consistently optimized for learning.

Personalized Learning Orchestrator  – orchestrates the learner’s other components and personalizes to the learner using machine learning based on the learner’s preferences, and performance as the course progresses.

Continuous Knowledge – acquires relevant knowledge continuously on any set of topics from any collection of sources, auto synthesizes with existing content in a variety of forms.

There are other important actors including peers and learning communities whose role can also be facilitated by the framework learning according to the learning objectives of the course leveraging them appropriately.(2)

The author tries to support his research by presenting what various challenges that could be encountered by AI and pathways on how these could be addressed. 

A study objective was not presented but lays down the foundation for how one may approach AI in learning. The title and the purpose were too generalized such that the reader would be unable to determine who would benefit most from AI, although the article presented graphics by showing maps and workflows of how AI would fit into the learning process and make it easier for the reader to interpret. 

The discussion introduced various literature regarding AI which would help the reader understand how and in what ways AI may help in learning and why it is a preferred route for the future

References:

1. Copeland, B. (2023, October 27). artificial intelligence. Encyclopedia Britannica. https://www.britannica.com/technology/artificial-intelligence

2. Srinivasan, V. (2022). AI & Learning: A preferred future. Computers and Education: Artificial Intelligence. https://doi.org/10.1016/j.caeai.2022.100062