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Although many types of intelligence exist (e.g., emotional, spatial, intrapersonal, interpersonal), this article deals with three intelligence concepts: artificial (AI), human, and hybrid. Before defining hybrid intelligence, it is necessary to understand the particularities of human and artificial intelligence definitions.
Intelligence
According to the RAE (n.d.), the word intelligence refers to the “ability to understand or comprehend; ability to solve problems.”
Human Intelligence
Human intelligence allows individuals to understand, reason, learn, solve problems, analyze situations and stimuli, create, and imagine. Some distinguishing characteristics of human intelligence are:
- It is biological and general.
- It learns through experience and socialization.
- It experiences emotions.
- It allows for awareness of one’s existence.
- It has tacit knowledge.
- It uses analogical thinking.
- It permits intuition.
- It considers the physical and biological aspects of the brain and the nervous and sensory systems.
Artificial Intelligence
Artificial intelligence (AI) can be defined according to theoretical, practical, or philosophical perspectives. However, the theoretical one, taken as more general, describes AI as a field of research and development that builds computational systems that simulate human learning, understanding, problem-solving, decision-making, creativity, and autonomy to perform tasks.
Some prominent features of AI are:
- Humans create it.
- It is specialized (task-centered) and uses algorithms.
- Its performance levels (speed, volume, and execution range) are second to none.
- It learns (trains) by analyzing big data.
- It can develop its learning logic.
- It does not possess tacit knowledge.
- It does not experience emotions.
- If there is no diversity in the data, it can lead to bias.
- It does not have systemic thinking like humans.
What is Hybrid Intelligence?
Hybrid intelligence ( HI) can be defined as the ability to achieve complex goals by combining human and artificial intelligence to achieve superior results and continuously improve by learning from each other.
In addition, hybrid intelligence as a field of study focuses on research and the design of ecosystems related to humans and AI, particularly user interactions. Another definition describes hybrid intelligence as creating synergistic, proactive, collaborative systems to achieve shared goals.
So, hybrid intelligence has two outcomes: human-augmented AI and augmented human intelligence. The former refers to human-trained AI systems that humans continuously improve, while the latter describes the result of human-AI combinations to enhance and elevate human intelligence.
What is required to develop a HI system successfully?
Some authors argue that optimal development of hybrid intelligence systems must overcome the following challenges:
HI in education
A hybrid intelligence approach to education can benefit the teaching-learning process, although it depends on the educational context and its level of applicability. Hybrid intelligence applied to education might have the following advantages:
- Improve the capacities of teachers and students through support with learning activities and experiences.
- Personalize learning through adaptability of content, learning style, etc.
- Promote inclusive education.
- Allow teachers to be co-creators and not just users of AI.
Along these lines, Holsten, Aleven, and Rummel (2020) describe a framework that describes hybrid human-AI adaptability in education. This approach contains four key benchmarks, described below:
These authors briefly describe the objectives of this conceptual framework as follows:
- Goal augmentation refers to teachers’ and educational AI systems’ mutual influence through goal alignment. This involves systems reporting on human goals (of the teachers) and vice versa (teachers inform AI) to improve educational outcomes.
- For example, AI can help the reflection process to improve objectives, especially when they conflict with instructional best practices.
- Perceptual augmentation improves the teachers’ and AI’s ability to perceive information that is important to instruction. It means expanding the information to what the other can receive, guiding how the child distributes their attention and how information is interpreted. At this point, systems can be designed to help teachers interpret, extract, or mediate inferences from their observations.
- For example, AI systems can provide data-driven insights and real-time feedback, while teachers provide contextual and experiential knowledge.
- Action augmentation results from the collaboration of humans and AI to improve and expand capabilities related to instructional actions. In other words, it enhances the capacity for action and expands the actions that can be carried out, as well as scalability.
- For example, AI to teachers: provide adaptable educational resources; Teachers to AI: develop tools that allow non-programmers to create or modify instructional content for educational AI systems.
- Decision augmentation results from collaboration to improve pedagogical decision-making. Its objective is to effectively correlate perception (what is observed) with action (what is performed).
- For example, Provide real-time feedback or suggestions to teachers.
Hybrid intelligence is a relatively new concept in education that must be explored, examining its operational functionality where reality is woven: in the classrooms. Its implementation will depend mainly on the educational context because access to technology is challenging in many academic institutions. Moreover, it is essential to consider the role of ethics in these new approaches, such as how implementing and deploying hybrid education will follow ethical regulations.
In conclusion, what I consider essential and refreshing is that hybrid intelligence involves AI’s human-oriented approach, its premise being to augment and not replace.
Translation by Daniel Wetta
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This article from Observatory of the Institute for the Future of Education may be shared under the terms of the license CC BY-NC-SA 4.0 
