CATSIB is a differential item functioning (DIF) assessment methodology for computerized adaptive test (CAT) data. Kernel smoothing (KS) is a technique for nonparametric estimation of item response functions. In this study an attempt has been made to develop a more efficient DIF procedure for CAT data, KS-CATSIB, by combining CATSIB with kernel smoothing. A correction for smoothing in boundaries is also implemented. It is hoped that such a methodology could provide a more powerful DIF technique for smaller samples while enhancing the interpretation of local DIF analyses. A simulation study was conducted to investigate the DIF estimation bias of KS-CATSIB in comparison to CATSIB with small samples. Sixteen DIF items varying in difficulty and discrimination were considered for this purpose. A sample of 500 examinees was used in the reference group and a sample of 250 examinees was used in the focal group. Preliminary results show that the correction for smoothing in boundaries, even though effective in reducing the bias in estimation, is still larger for KS-CATSIB in comparison with CATSIB. Therefore, DIF estimates associated with KS-CATSIB are statistically biased and would lead to high Type I error rates. Further modifications of KS-CATSIB are necessary before the program is ready for full implementation. (Contains 3 tables and 13 references.) (Author/SLD)