Mohammad Ratul Mahjabin

Mohammad Ratul Mahjabin, Raiyan Abdul Baten

General intellectual humility (GIH) -- the recognition that one's beliefs may be fallible and revisable -- is associated with improved reasoning, learning, and social discourse, yet is widely regarded as a stable trait resistant to intervention. We test whether GIH can be elevated through a conversational intervention that combines staged cognitive scaffolding with personalized Socratic reflection. In a randomized controlled experiment (N=400), participants engaged in a structured, LLM-mediated dialogue that progressed from conceptual understanding of intellectual humility to applying, analyzing, evaluating, and generating novel, self-relevant scenarios that instantiate it. Relative to a time-matched control, the intervention produced a systematic increase in GIH, reduced rank-order stability, and tripled the rate of reliable individual improvement. Crucially, these effects persisted over a two-week follow-up without detectable decay. The effects generalized across political affiliation and did not depend on baseline personality profile. These findings challenge the prevailing pessimism regarding the malleability of GIH and suggest that scaffolded, Socratic reflection delivered through structured dialogue can produce durable changes in general intellectual humility.

Mohtasim Hadi Rafi, Mohammad Ratul Mahjabin, Md Sabbir Rahman

One of the biggest challenges that the farmers go through is to fight insect pests during agricultural product yields. The problem can be solved easily and avoid economic losses by taking timely preventive measures. This requires identifying insect pests in an easy and effective manner. Most of the insect species have similarities between them. Without proper help from the agriculturist academician it is very challenging for the farmers to identify the crop pests accurately. To address this issue we have done extensive experiments considering different methods to find out the best method among all. This paper presents a detailed overview of the experiments done on mainly a robust dataset named IP102 including transfer learning with finetuning, attention mechanism and custom architecture. Some example from another dataset D0 is also shown to show robustness of our experimented techniques.