Ethics-by-design plays a crucial role when defining a better learning ecosystem to increase human capabilities, accepting that algorithmic thinking is evolving so fast.
Algorithmic thinking can be built into learning environments as the ability to be adaptive (create and change learning structures), efficient (use of learning resources provided), and methodical (a structural approach to solving problems).
Ethics-by-design is a very interesting construction to apply even before we start designing learning environments with artificial intelligence algorithms. It is necessary to align with compliance and privacy laws, informing students about how the artificial intelligence algorithm will process their personal information.
Furthermore, when users are involved in learning decisions, for example, defining how they would like to learn, encouraging human cognitive autonomy allows individuals to adapt to dynamic learning scenarios, creating and/or improving them.
For this purpose, learning resources could be structured or non-structured. Non-structured learning resources (specific materials) are useful for individuals to choose and design their learning paths. Structured learning resources (specific learning paths) are useful to follow predefined processes or to improve them.
Following this perspective, we could be able to design a learning ecosystem where algorithmic thinking and human cognitive autonomy can match positively to innovate when new learning approaches improve existing ones.
By respecting human cognitive autonomy through ethics-by-design and balancing human evolution with algorithmic realities, we can enhance human capabilities.
Specific challenges should be considered, such as the level of autonomy and algorithmic thinking that is currently developed in education (how students adapt to rapidly evolving technology in learning) and in business learning models (how organizations develop innovation).
The quality of learning when measuring the potential expansion of new paradigms in practical evolution (transfer of learning) becomes a critical metric, especially if we consider that collaborative learning adds valuable data and information through diverse perspectives and co-creation.
The benefits are to foster an environment where innovation in learning is driven by ethical considerations, ensuring that as technology advances, education remains a human-centric endeavour, allowing transformational evolution.
As a conclusion, it would be reasonable to design “algorithmic thinking” as a universal tool in dynamic learning environments, encouraging students to customize their learning journeys according to the evolution of reality when ethical considerations also evolve.
This assumption will probably require ‘just in time’ ethical constructions and adjustments due to the scalability of technology beyond the law’s capacity to update compliance considerations.
Finally, an open question: Is a constant focus on transformational innovation through human cognitive autonomy that crucial to face the challenges of an ever-evolving reality?
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