While I’m sceptical of their experiment research design*, the concept of metacognitive laziness from this paper is clearly a useful contribution to thel literature. As Fan et al define it, this refers to “earners’ dependence on AI assistance, offloading meta – cognitive load and less effectively associating responsible metacognitive processes with learning tasks”. This matters because “offloading metacognitive effort to AI tools results in less effective engagement with essential self-regulatory tasks,” (pg 506). The risk is not just the offloading itself, it is increased passivity in the wider process of which the offloaded tasks are part.
This can undermine self-regulated learning because the metacognitive requirements for doing this effectively (e.g. goal setting, self-monitoring, self-evaluative etc) can be eroded over time by a reliance on the AI to negotiate difficulty. As they summarise the risk on pg 492:
the tendency of learners to become over-reliant on AI poses challenges for hybrid intelligence. This issue aligns with the concept of cognitive offloading, as proposed by Risko and Gilbert (2016), where learners delegate cognitive tasks to external tools to reduce cognitive effort. Although cognitive offloading can be beneficial in managing cognitive load, it may lead to decreased internal cognitive engage- ment over time, ultimately impacting learners’ ability to self-regulate and critically engage with learning material (Risko & Gilbert, 2016). Such cognitive offloading can lead to habitual avoidance of deliberate cognitive effort, a phenomenon echoing the emergence of what we term metacognitive laziness. From a more theoretical perspective, Alter et al. (2007) demonstrated that metacognitive experiences of difficulty or disfluency activate more analytical reasoning processes. When learners encounter situations that challenge their intuition, they are more likely to engage in deliberate analytical thinking (i.e., System 2 processes) (Alter et al., 2007). In the context of GenAI, if learners rely excessively on AI-generated outputs or facilitation, they might not experience the necessary disfluency or cognitive difficulty to trigger these deeper metacognitive processes.
The experience of difficulty activates metacognition. If the students cognitively outsource in increasingly habitual ways, it doesn’t just mean they lose the learning involved in what they are outsourcing. It means they lose their capacity to tolerate difficulty, as well to respond metacognitively to that difficulty. This points to the assumption which many educators have that there is something fundamentally corrosive in how students relate to AI which carries a threat exceeding the particular risks for any one assignment. This is a really sharp conceptualisation of the epistemic risk for learning involved in generative AI which gets beyond some of the limits of the ‘cognitive offloading’ concept.
*It seems fundamentally implausible to operationalise intrinsic motivation in the context of an experimental study. If you reduce motivation into the student’s expressed engagement with discrete tasks then it’s been quite dramatically circumscribed to fit the experimental constructs. Furthermore, we urgently need longitudinal studies in order to make meaningful claims about things like ‘cognitive off-loading’, ‘skill atrophy’ and ‘metacognitive laziness’. These just aren’t things which can be studied adequately at the level of discrete tasks, particularly ones that have been designed by a research team and have no real stakes for participants.
Mark Carrigan 