There are many ways to assess learning. The assessment must happen timely, if not instantly. Reducing the time gap between the task and the evaluation is essential regarding motivation and education. AI and ML can help us achieve this in ways we could only dream of in the past.
Assessment and taxonomies
We will see a reformulation of well-known taxonomies such as Blooms’ digital taxonomy, Fink’s taxonomy of significant learning, and Biggs’ SOLO taxonomy. Future learning taxonomies will focus on elements borrowed from games, and they will take into account that ubiquitous learning and connectivism are a reality.
The assessment will still be competency-based, and the accuracy will be far better than today due to the capabilities of AI. The modules of PresentPastFuture will contain gamified micromodules with timelines, progress bars, badges, and similar visual clues helping the pupils understand where to go next and what to do. The tasks will adjust to match the pupil’s ZPD perfectly, and «There will be a relatively low cost of failure and high reward for success» (James Paul Gee, 2007).
If a skill is learned, you cannot «unlearn» it.
Arne Midtlund
The AI-driven assessment system reflects that: curiosity, imagination, contribution, and creativity are essential. It is vital that the pupils have fun while learning, and they have to be rewarded for their effort and when they complete tasks. «On balance [the] experience needs to be pleasurable» (Salen & Zimmerman, 2004).
Cool and gamified
The grading systems are inspired by games where you can only progress through different levels of competence within the ZPD. Positive reinforcement and scaffolding will lead to more motivation. And the tightly integrated system of assignments and assessments will contribute to motivation according to the theory of flow and the Self Determination Theory.
The principles of positive reinforcement are made possible by dividing the curriculum into several micromodules. The teachers assisted by AI decide the difficulty levels. It’s only possible to get better, to progress through different tasks. If a skill is learned, you cannot «unlearn» it.
