Table 7 A SWOT Analysis of AI in smart classes
Strengths | Weaknesses |
|---|---|
Continuous environment monitoring through sensors that results in an optimized learning environment Enhanced Interactivity, including immersive experiences Adaptability to individual needs of students On sight/remote/mixed class delivery | No integrated smart class technology offered Equipment Cost Need for student/teacher expertise in using emerging technologies Need for large amounts of data to train systems Separation and disengagement from the learning process. That results in isolated students |
Opportunities | Threats |
|---|---|
Availability of state-of-the-art equipment at more accessible cost (i.e. interactive screens, cameras, microphones, VR and AR headsets and glasses) External factors, like the COVID-19 pandemic, dictate the use of technology in teaching as a means of supporting remote teaching Trend towards on-line virtual environments (i.e. META, METAVERSE (Mystakides, 2022) in line with smart-class technologies Latest development in AI that results in accurate algorithms, in the form of deep learning. Availability of ‘public’ ML tools (i.e. lobe.ai, that allows not trained individuals to set up and use ML models) | Privacy issues, ethics and GDBR regulations regarding data collection required by smart systems AI systems and large server stations that store data regarding vital research, may be threatened by hackers Teachers tend to avoid or face difficulties using AI systems due to their inadequacy to adapt to new forms of technology and refuse to accept new technologies as a new norm Cheating-based AI tools may give an unfair advantage to students over their classmates during exams and assessments (Abd-Elaal et. al., 2019) Bias in ML systems that may cause unfair student treatment |