As we continue to ask AI to support us in tasks, including assessing learners, it is helpful for us to think about the types of thinking or reasoning that AI can engage in. In the literature, there are three key types of reasoning: inductive, deductive, and abductive. These reasoning methods are no longer just for humans, they also help us understand what AI is capable of – and where it struggles.
1. Inductive Reasoning
Inductive reasoning is about spotting patterns and making general conclusions based on specific examples. For instance, if you see 1,000 white swans, you might conclude, “All swans must be white.”
Of course, this works most of the time, but it doesn’t account for the odd exception (like a black swan).
2. Deductive Reasoning
Deductive reasoning goes in the other direction: it starts with a general rule and applies it to a specific case. If the general principle is true, then the conclusion is guaranteed to be true too. Let’s say the rule is “All mammals have lungs.” If your pet is a mammal, you can deduce that your pet has lungs.
3. Abductive Reasoning
Abductive reasoning is a bit trickier. It’s about making the best guess based on limited information—essentially forming a hypothesis when you don’t have all the facts. It’s often how we explain surprises or unexpected events. For example, if you come home and your front door is wide open, you might guess someone’s been inside, even if you don’t have all the evidence.
So, what can AI handle, and where does it struggle?
Inductive Reasoning: AI shines when it comes to inductive reasoning. By processing huge amounts of data, AI can recognize patterns and trends that help make predictions. This is why AI tools are great at suggesting movies, predicting weather, or identifying faces in photos.
Deductive Reasoning: AI also handles deductive reasoning quite well, especially when there are clear rules to follow. This is why AI can beat humans at chess or solve complex problems in structured environments.
Abductive Reasoning: Where AI struggles is with abductive reasoning—the ability to form a hypothesis when the information is incomplete or surprising. AI can’t quite match the human intuition that helps us make those quick, creative leaps based on limited clues. While AI can generate hypotheses, it’s still a work in progress when it comes to handling truly unexpected situations or making creative guesses.
AI has come a long way in mastering inductive and deductive tasks. But when it comes to abductive reasoning, humans still have the edge. The human ability to hypothesize and think creatively from limited information remains hard for machines to mimic. As AI evolves, it may get better at this, but for now, human intuition remains one of our key advantages over machines.
