#AI@Work: AIEd Systems Provide Evidence
The mass of data collected in AIEd systems can help to provide evidence for viable approaches. Just in Time (JIT) and PoW (Point of Work) feedback will interact with learner challenges, successes and needs. Assessment and evaluation will take place while learning is taking place, instead of after the fact. New discoveries in psychology and neuroscience will support a better understanding of learning. AIEd needs to create comfortable and motivated lifelong learners. Educational models and systems need to change. Most significantly, there will be more questions than answers as AIEd moves forward.
The sharing of data is imperative to AIEd moving forward. AIEd has the potential to reach students and help close the achievement gap. The gap exists between learners from more affluent and poorer backgrounds. It has the potential to measure educational system achievement in ways that are unique. AI will be able to provide feedback and analysis on every level of teaching and learning. It won’t matter whether it is a class, a lesson, a subject, a university, state or country. Educational systems will need to be agile and committed to reform. It’s all about the learning, not about AI technology alone. The evolution needs to be funded, supported, innovative and collaborative. Standards will play an important role in AIEd. More teachers, professors, trainers and learners need to be involved in the development of AIEd tools. They especially need to be involved in developing the rules that will govern ethics and data sharing.
ASK THE HARD QUESTIONS
• How is AI being used in training and education today?
• How might AI help underprivileged learners?
• How to we guard privacy and personal information in AIEd?
• Can our current educational systems adapt to AI?
• How do you feel about AI for education and training?
#AI@Work, #WFH, #Virtual Touchpoints, #ThePajamaEffect, #The Visual Connection, #BobbeGB, #BobbeBaggio, #Touchpoints, #Remote Workplace, #WorkFromHome, #PJEffect, #LinkedInNewsLive
