The "Smart" in Smart Learning Environments

1. What makes a "Smart" SLE?

In his paper "Smart Learning Environments: Concepts and Issues", Spector states:

"The term ‘smart’ has been used to modify a learning environment as well as a technology. A learning environment includes technology, so it seems appropriate to include both in this discussion. A learning environment is the broader concept addressed in this paper, and it includes students, instructors or an instructional system, the settings in which learning occurs, the support staff including designers and technical specialists, as well as the culture of the class, course, institution, and community. Based on characteristics of human intelligence that might transfer to technologies and learning environments, a smart learning environment is one that has several of the following characteristics: 

• Knowledge - access to pertinent information and the ability to add or modify that information;
• Task support – the ability to perform a task or provide a learner with tools and information needed to perform a task;
• Learner sensitivity – the ability to maintain and make use of a profile of the learner so as to provide appropriate support and knowledge; 
• Context sensitivity – the ability to recognize specific situations, including those situations in which a learner might be in need of assistance; 
• Reflection and feedback – the ability to critique a solution or performance and/or provide meaningful and timely feedback to a learner based on the learner’s progress and profile and the learning task at hand." (Spector, 2016)

Table 1. Dimensions of Smart Characteristics (Spector, 2016)

Context sensitivity	Automatic access and update capability; learner control over access and updates; the ability to determine when a is struggling; the ability to find and make use of related solutions or approaches
Reflection and feedback	Automatic access and update capability; learner control over access and updates; the ability to generate meaningful critiques and constructive feedback

Optional Reading:
Please take some time to read " Smart learning environments, and not so smart learning environments: a systems view "  by Jon Dron. 

2. AI in Education

The advancements made in the field of Artificial Intelligence AI have impacted education in a variety of ways. With the success of early systems, like Expert Systems and Intelligent Tutoring Systems ITS, a higher impact was anticipated since the rise of statistical approaches in Machine and -its subset- Deep Learning.
Nafea shows in her publication "Machine Learning in Educational Technology" how education is making the most of ML through Virtual Assistants for example (Nafea, 2018).
Many efforts are now dedicated to harnessing the power of Natural Language Processing in educational fields, as this allows programs to communicate with learners in a natural language, closely imitating human communication. The field of language learning is one of the hot topics benefiting from the power of NLP. Educational Testing Services is active on the research bridging NLP and language learning, please consider looking at one of their NLP-based products here: https://www.ets.org/research/topics/as_nlp/educational_applications/

References:

Spector, J.M. (2016). Smart Learning Environments: Concepts and Issues. In G. Chamblee & L. Langub (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference (pp. 2728-2737). Savannah, GA, United States: Association for the Advancement of Computing in Education (AACE). Retrieved January 23, 2020 from https://www.learntechlib.org/primary/p/172078/.

Ibtehal Talal Nafea (September 19th 2018). Machine Learning in Educational Technology, Machine Learning - Advanced Techniques and Emerging Applications, Hamed Farhadi, IntechOpen, DOI: 10.5772/intechopen.72906. Available from: https://www.intechopen.com/books/machine-learning-advanced-techniques-and-emerging-applications/machine-learning-in-educational-technology