Virtual interviews: A new norm in qualitative research during the Covid-19 pandemic
Posted: Mon Jan 27, 2025 10:05 am
This blog post looks into the current research on these empathetic digital teaching assistants by summarising a thorough review of studies led by Ortega-Ochoa et al. (2024). The review examined 1,162 studies from databases such as Scopus and Web of Science, covering research from 2018 to 2022. Out of these, 13 studies were selected for a detailed analysis based on criteria including relevance and quality. The summary below explains how these assistants are developed and how their success is measured.
Design principles of empathetic digital teaching assistants
The main design features include a strong emphasis on the ability to understand and share the feelings of students, encouraging conversations that aid learning, expertise in the subject matter, and customised feedback that matches azerbaijan consumer email list the student’s learning level. For example, Kumar (2021) described an assistant that can interact in a very human-like way, using personal greetings which helps build a connection. Another important feature is how these assistants can use information about what a student already knows to make the learning experience more personalised and effective. For instance, Wu et al. (2020) used initial tests to understand each student’s knowledge level, allowing the assistant to adjust its interactions for more personalised support.
Design principles of empathetic digital teaching assistants
The main design features include a strong emphasis on the ability to understand and share the feelings of students, encouraging conversations that aid learning, expertise in the subject matter, and customised feedback that matches azerbaijan consumer email list the student’s learning level. For example, Kumar (2021) described an assistant that can interact in a very human-like way, using personal greetings which helps build a connection. Another important feature is how these assistants can use information about what a student already knows to make the learning experience more personalised and effective. For instance, Wu et al. (2020) used initial tests to understand each student’s knowledge level, allowing the assistant to adjust its interactions for more personalised support.