Bring the Student Closer to Success, with Artificial Intelligence Assistants!
\nArtificial Intelligence (AI) assistants are designed to guide any level of student that has fallen behind the mainstream curriculum. 2030 approaches, and these AI assistants are being added to the standards of education. Students that are failing to maintain satisfactory progress will be provided with an AI assistant to help them in that subject. Here, the AI assistant will be set by the teacher, to the needs of the student. The AI assistant will not only track and maintain the student\u2019s progress, but the assistant will also free up the teacher to continue with the main classroom teaching.
\nTaneri (2020, p. 3) noted that students could receive tailored education anywhere in the world. The AI assistant to offer additional attention to the subject should be identified as an extension from the teacher to the student, not as the removal of the teacher from the education equation. In a world without physical boarders, students may be able to learn from any location using any device. Teachers are now able to assist students through the regular stream of curriculum, while AI can adjust the learning curve for the students sitting above and below the median by tracking progression of the student as well as adjusting curriculum to set pace of the student. Improved confidence builds on students\u2019 self confidence, while building on learning without direct instruction from the teacher (Enfield, p. 22). Results are only as good as the database, which will reflect directly on the AI assistant being able to offer best answers (Taneri, (2020, p. 3). The student will have an increased knowledge base that stems from the database, with collected data from past students, where the AI will filter out failed answers from previous programs or events. Students no longer attend school physically, 2030 will completely be an online experience. In 2030, an internet-ready device and stable connection will provide the student with access to the classroom without the need to travel. Bring your own device (BYOD) has increased the ability of remotely located students to be educated. 2030 will indubitably provide the students requiring AI assistants to catch up on curriculum, through utilisation of information to the best needs of the student. While classroom settings will be of the past for most students, 2030 offers variety of devices to connect to this virtual classroom allowing the instruction to be based out of any location. AI should not be posthuman (Leonard, 2020), where nonhuman interaction is created by the human. Time spent setting the AI assistant to the student will be offset, over the course of a term in which the teacher will not be distracted from presenting the mainstream curriculum to the class. Course curriculum in 2030 with the realization of full-time AI use, allow an educator to be dedicated to the mainstream students. The AI instruction to the student remains monitored by the human instructor. When a student interacts with the AI assistant, the teacher will be aware of the communications. This will keep the teacher abreast of the student\u2019s activities and advancement in the program.
\nUtilizing the inclusive database that is derived through many years of student participation, will be implemented into the AI assistant to provide better guidance to future students regardless of future education pursuits. The AI assistant\u2019s vast database will encompass all levels of student, from grade school through vocational and university-level education. The teacher will be able to prepare the AI assistant to the students\u2019 abilities and shortcomings, with set focus on the educational direction of the student towards assignment or program completion. Park and Shea, 2020, pp. 226-227) teacher interaction with the student and AI, provides a strong outcome so long as all participants are engaged in working towards a common goal. If any contributor fails, the group too fails. Presenting the AI assistant this solidly loaded database of the best practices for relaying content will provide long-term positive results to the students. An interactive pedagogical approach through the AI offers the best possible solutions from a didactic methodology (Maher & Tetreault, 1999). The AI assistant will offer best direction and answers to the students\u2019 needs. Curriculum changes are also made possible within the database due to input of data from AI assistant which oversees the student and makes a course correction with this new data. Database changes made by any teacher will be implemented throughout the AI assistants\u2019 programming, providing new or improved answers into the database for future students. Teachers and students will see the AI assistant from different points of view. Through the eyes of an educator, the AI may lack a connection to the student where a teacher can understand the needs of a student (Aksu & Durak, 2019, p. 167).
\nGamification pedagogy will draw interaction and engagement with the students to content, making learning again fun while educational (Vinichenko et al., 2019). Optimization of the AI to interface with the student will garnish a learning environment that is not only fun but educational as well. Elevated student interest with interaction of using the AI assistant, will also gain better data that can be added back to the database. In 2030, AI will no longer be in an incubation stage (Chatterjee & Bhattacharjee, 2020). Advancements in technology by 2030 will expedite communication from AI to database. This progression of communication between the AI assistant and the database will increase students\u2019 overall success. Positive accomplishments will place the student back into the standard curriculum presented by the teacher. Thus, the cycle of needing the AI assistant is complete. Should the student again in the future require the extra attention provided by AI, the teacher will only need to reactivate the program for that student. Success in the classroom in the future sounds amazing, with the inclusion of AI assistants! Conclusion
\nSpeculation into 2030 for the implementation of AI, has been an interesting process to fathom. Prediction of an open database location for all levels of instruction may not come to fruition, where this data would set the AI assistant towards success in every level of education. For the AI assistant in 2030, a broad and in-depth database ensures all student-level concerns are met with an appropriate and timely response. Teacher-level responses too would be measured and weighed through the database to the student, through input not limited to former student performance. Dissemination of information into the database will encourage the AI to predict the correct response to the wayward student. The teacher will also be able to interact with the AI following the progress of the student, with guidance the AI may be changed to interact in a different fashion to a particular student. 2030 is a future that approaches quickly. A database set of precise data is paramount to allow the AI assistant to flourish with regards to execution of interaction with the student. Teachers will be only augmented by AI, not replaced. 2030 remains a mystery, except that teachers and students in this timeframe persist as fixed participants in the future of education. Remember the AI is a tool. Education of a student resides with the teacher, who uses the tool (AI) to provide an alternative to the mainstream curriculums.<\/p>\n
References
\nAksu, F. N., & Durak, G. (2019). Robotics in education: Examining information technology teachers\u2019 views. Journal of Education and E-Learning Research, 6(4), 162-168. https:\/\/doi.org\/10.20448\/journal.509.2019.64.162.168
\nChatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25(5), 3443-3463. https:\/\/doi.org\/10.1007\/s10639-020-10159-7
\nEnfield, J. (2013). Looking at the impact of the flipped classroom model of instruction on undergraduate multimedia students at CSUN. TechTrends, 57(6), 14-27. https:\/\/doi.org\/10.1007\/s11528-013-0698-1
\nLeonard, N. (2020). Entanglement Art Education: Factoring ARTificial Intelligence and Nonhumans Into Future Art Curricula. Art Education, 73(4), 22-28. https:\/\/doi.org\/10.1080\/00043125.2020.1746163
\nMaher, F., & Tetreault, M. K. (1999). Knowledge versus Pedagogy: The Marginalization of Teacher Education. Academe, 85(1), 40\u201343.
\nPark, H., & Shea, P. (2020). A Ten-Year Review of Online Learning Research through Co-Citation Analysis. Online Learning, 24(225-244). https:\/\/doi.org\/10.24059\/olj.v24i2.2001
\nTaneri, G. U., (2020, June). Artificial intelligence & higher education: Towards customized teaching and learning, and skills for an AI world of work. Research & Occasional Paper Series: CSHE.6.2020. Berkeley Center for Studies in Higher Education. https:\/\/eric.ed.gov\/?id=ED606654
\nVinichenko, M. V., Ridho, T. K., Karacsony, P., Li, N. P., Narrainen, G. S., & \u0421hulanova, O. L. (2019). The Feasibility and Expediency of Gamification Activities in Higher Education. International Journal of Education and Practice, 7(4), 459-468. https:\/\/doi.org\/10.18488\/journal.61.2019.74.459.468<\/p>\n","protected":false},"excerpt":{"rendered":"
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