Indeed, it’s challenging to hunt for the definition of “Empathetic learning” in terms of pedagogy, however, general concepts popped up when attempts were made to look for the learning where emotions and feelings are prioritized over the implementation of methodology. In other words, as we continue to advance the applications and capabilities of AI in classrooms, debate is emerging around whether we can or even should imbue these systems with what are considered uniquely human emotions, such as empathy.
Even though Artificial Intelligence has been playing crucial role in making education system the most effective, areas have been found where the role of robotics seem insignificant as far as outcome is concerned. A very clear example is cited in this paper-“Less public attention has been given to other areas of AI in social policy, especially AI in education (AIEd). This paper attempts to address that gap by characterizing and then exploring the implications of AIEd as well as providing recommendations to AI researchers and developers towards a responsible research agenda. Moreover, this analysis brings a forward-looking policy lens that can help situate the work of AI developers and researchers in the larger picture.
One of the major points of discussion in using AI in classrooms is that AI speaks the mind of researchers and developers and tell us the hidden state of mind of society. Blanchard says, “Implicit and explicit biases in AIEd development also reflect the slanted demographics of its researchers—their own backgrounds and assumptions about education are already shaping AIEd (Blanchard 2015).
I believe, the futuristic learning would be called complete when Knowledge, Skills, Aptitude and attitude learning objectives would be accomplished. In this era, We have touched the mark of Knowledge, Skills and Aptitude under the facilitation of machine learning, and strongly opine that the target is close to achieve when need based learning would be done with the AI systems. As Daniell Krettek rightly said, ” Our researchers and developers are continuously progressing on the improved versions of robots so that empathy, feelings or emotions can be embedded to make the system efficient to rely upon for the educational needs.
Following are my questions that have been developed while researching on this project-
The kind of experiments which are being done with insufficient AI systems – What are the outcomes or long term repercussions of mistakes or biasness shown by robot or chatbots publicly?
What impact does it have on tiny toddlers or upcoming educators , to whom some new concepts are being taught with biased codes embedded in machines ?
Certainly, there is a key for every lock, likewise, proposed solutions is considering user’s private choices in public policy decisions that will not only bring transparency in the system about one another’s choices but also give a glimpse about the kind of assumptions and mindset the learners have. Also all Artificial Intelligence systems have privilege to customize the outcome and coding depending upon the requirement, that, obviously, require strong coordination and a lot of brain storming on the part of educational technologists present in the educational institutions.
Additionally, monitoring the content in AI is another way that can keep the control in the hands of administration that would help them decide to cut or crop certain bits or bytes to be removed or be hidden from the systems that can create or reflect any kind of biasness in the classrooms or on Learning Management Systems. Without teachers (or teaching), we have good reasons to think that the quality gap between traditional and digital education will not be so easily closed and hence implementation will fall utterly short of the hopes of educational technology’s strongest proponents. This is why AI is so enticing: it offers to close the quality gap by tackling the most insoluble barrier—teaching.Footnote7 I argue that the central ambition of modern AIEd is to simulate teachers
To mitigate this effect, mentoring strategies could be developed to support researchers less skilled at developing and presenting human-based evaluations. Another option for the community would be to consider the norm as inadequate and propose measures to control the importance of human-based evaluations. For example, conference tracks targeting humanities-related papers, technology-related papers, or hybrid papers could be proposed and implement different paper selection approaches.
References
Schiff, Daniel. “Out of the Laboratory and into the Classroom: The Future of Artificial Intelligence in Education.” AI & SOCIETY, Springer London, 9 Aug. 2020, https://link.springer.com/article/10.1007/s00146-020-01033-8#Sec8.
Selwyn, Neil, and Petar Jandrić “Postdigital Living in the Age of Covid-19: Unsettling What We See as Possible.” Postdigital Science and Education, Springer International Publishing, 9 July 2020, https://link.springer.com/article/10.1007/s42438-020-00166-9.
International Society for Technology in Education. (n.d.). ISTE standards for coaches. International Society for Technology in Education. Retrieved October 30, 2021, from https://www.iste.org/standards/for-coaches
Ruha Benjamin, “Introduction,” Race after Technology: Abolitionist Tools for the New Jim Code (Medford, MA: Polity, 2019), 1-32
Blanchard, Emmanuel G. “Socio-Cultural Imbalances in AIED Research: Investigations, Implications and Opportunities.” International Journal of Artificial Intelligence in Education, Springer New York, 13 Sept. 2014, https://link.springer.com/article/10.1007/s40593-014-0027-7#Sec12.
