The Support, Educate, Empower personalized glaucoma coaching trial design.

BACKGROUND: Glaucoma is a chronic disease that affects 3 million Americans. Glaucoma is most often asymptomatic until very late in its course when treatment is more difficult and extensive peripheral vision loss has already occurred. Taking daily medications can mitigate this vision loss, but at least half of people with glaucoma do not take their prescribed medications regularly. The purpose of this study is to improve glaucoma medication adherence among those with medically treated glaucoma and poor self-reported adherence using the Support, Educate, Empower personalized coaching program. METHODS/DESIGN: This study is a two-site randomized controlled trial enrolling 230 participants with poor self-reported glaucoma medication adherence. The trial has two arms, an intervention arm and a control arm. Participants in the intervention arm receive personalized glaucoma education and motivational interviewing-based coaching over 6 months from a trained non-physician interventionist for three in-person sessions with between visit phone calls for check-ins where current adherence level is reported to participants. Participants also can elect to have visual, audio, text or automated phone call medication dose reminders. Participants in the control arm continue usual care with their physician and receive non-personalized glaucoma educational materials via mail in parallel to the three in-person coaching sessions to control for glaucoma knowledge content. All participants receive a medication adherence monitor. The primary outcome is the proportion of prescribed doses taken on schedule during the 6-month period. The secondary outcome is glaucoma related distress. The exploratory outcome is intraocular pressure. DISCUSSION: The personalized education and motivational-interviewing-based intervention that we are testing is comprehensive in that it addresses the wide range of barriers to adherence that people with glaucoma encounter. Leveraging a custom-built web-based application to generate the personalized content and the motivational-interviewing-based prompts to guide the coaching sessions will make this program both replicable and scalable and can be integrated into clinical care utilizing trained non-physician providers. Although this type of self-management support is not currently reimbursed for glaucoma as it is for diabetes, this trial could help shape future policy change should the intervention be found effective.

Effectiveness of an Intervention Providing Digitally Generated Personalized Feedback and Education on Adherence to Continuous Positive Airway Pressure: Randomized Controlled Trial.

BACKGROUND: Many people worldwide experience obstructive sleep apnea, which is associated with medical and psychological problems. Continuous positive airway pressure (CPAP) is an efficacious therapy for obstructive sleep apnea, but its effect is limited by nonadherence. Studies show that personalized education and feedback can increase CPAP adherence. Moreover, tailoring the style of information to the psychological profile of a patient has been shown to enhance the impact of interventions. OBJECTIVE: This study aimed to assess the effect of an intervention providing digitally generated personalized education and feedback on CPAP adherence and the additional effect of tailoring the style of the education and feedback to an individual's psychological profile. METHODS: This study was a 90-day, multicenter, parallel, single-blinded, and randomized controlled trial with 3 conditions: personalized content in a tailored style (PT) in addition to usual care (UC), personalized content in a nontailored style (PN) in addition to UC, and UC. To test the effect of personalized education and feedback, the PN + PT group was compared with the UC group. To test the additional effect of tailoring the style to psychological profiles, the PN and PT groups were compared. Overall, 169 participants were recruited from 6 US sleep clinics. The primary outcome measures were adherence based on minutes of use per night and on nights of use per week. RESULTS: We found a significant positive effect of personalized education and feedback on both primary adherence outcome measures. The difference in the estimated average adherence based on minutes of use per night between the PT + PN and UC groups on day 90 was 81.3 minutes in favor of the PT + PN group (95% CI -134.00 to -29.10; P=.002). The difference in the average adherence based on nights of use per week between the PT + PN and UC groups at week 12 was 0.9 nights per week in favor of the PT + PN group (difference in odds ratio 0.39, 95% CI 0.21-0.72; P=.003). We did not find an additional effect of tailoring the style of the intervention to psychological profiles on the primary outcomes. The difference in nightly use between the PT and PN groups on day 90 (95% CI -28.20 to 96.50; P=.28) and the difference in nights of use per week between the PT and PN groups at week 12 (difference in odds ratio 0.85, 95% CI 0.51-1.43; P=.054) were both nonsignificant. CONCLUSIONS: The results show that personalized education and feedback can increase CPAP adherence substantially. Tailoring the style of the intervention to the psychological profiles of patients did not further increase adherence. Future research should investigate how the impact of interventions can be enhanced by catering to differences in psychological profiles. TRIAL REGISTRATION: ClinicalTrials.gov NCT02195531; https://clinicaltrials.gov/ct2/show/NCT02195531.

Personalized Preclinical Training in Dental Ergonomics and Endodontics in Undergraduate Dentistry Students (Pilot Study).

The curriculum at the Department of Pathophysiology in the Periodontal Sciences program at Okayama University includes normative preclinical training (NPT) using phantoms. NPT is given to the whole class of 5 th year students divided in groups of 8 students/instructor. In 2019, an innovative personalized preclinical training (PPT) pilot study was implemented for this group of students whereby two students, each with their own dental unit, were coached by one instructor. The main topics covered were dental ergonomics and endodontics. We aimed to evaluate the effectiveness of PPT in dental ergonomics and endodontics toward increasing the knowledge and future clinical skills of students who had already undergone NPT. A test on endodontics was taken before and after PPT. A questionnaire was completed to assess their perception of improvement regarding the above-mentioned topics. Test scores and questionnaire results both showed that the students' level of knowledge and awareness of future clinical skills was significantly higher after PPT. This pilot study demonstrated that PPT increased the students' knowledge and future clinical skills. As preclinical training forms the foundation for clinical practice, investment in future research regarding this personalized approach is likely to enhance students' understanding and clinical performance.

Developing a personalized self-care curriculum for graduate students in the health sciences.

The purpose of this study was to evaluate the Pitt Personal Wellness Program, three 1-hour workshops that were integrated into the first-semester curriculum of five health sciences graduate programs. The workshops were designed as a personalized education approach to promote self-care and well-being. Of the 156 graduate students who participated in the Pitt Personal Wellness Program, 99 (65%) completed online questionnaires regarding stress and coping at three time points across a 14 week period: before the program, and after the second and third workshops. Graduate students reported significant decreases in perceived stress and number of stressful situations across the three time points. Students who reported a previous history of mental health counseling demonstrated steeper declines in perceived stress across Times 1 and 2, and reported significantly higher use of coping strategies across the three time points. Racially minoritized students and those reporting more financial struggles also reported significant declines in stress and number of stressors across Times 1 and 2. The Pitt Personal Wellness Program was acceptable to graduate students and a promising program for further dissemination and study.

[The influence of gender, age and length of service on educational preferences of general practitioners in Russia].

In conditions of active development of medical and pharmaceutical sciences and dynamically changing work environment the education of physician is to be both continuous and personalized. Purpose of the study. To investigate effect of gender, age and work experience on educational needs and on preferences of forms and methods of training in the system of continuing medical education of Russian general practitioners. The research bases were Chairs occupied with issues of personnel training for general medical practice in Moscow, Belgorod and Kemerovo. The sociological, statistical and analytical research methodologies were applied. The overall analysis covered 402 questionnaires for general practitioners. The sampling of respondents included 19.1% of men, 80.9% of women; 52.4% were aged 36-55 (60) years, 29.3% - 24-35 years and 18.3% - 56 (61) years and older; 45.0% had experience in general medical practice up to 2 years, 16.3% - 3-4 years, 27.0% - 5-6 years, 11.7% - 7 or more years. The results of comparative analysis of opinions of physicians of both genders and various age with different time of experience concerning level of significance for practice of various areas of competence within the framework of specialty and their preferences regarding organizational forms, training methods and its duration are presented. It is established that gender, age and length of service had no significant effect on the studied parameters. However, certain characteristics associated with these factors are identified. The further study of possible influence of other internal and external factors can contribute in the future into formulation of scientifically grounded approach to choosing personalized trajectory of education of general practitioner.

Educational Encounters of the Third Kind.

An engineer who becomes an educator in a school of software engineering has the mission to teach how to design and construct software systems, therein applying his or her knowledge and expertise. However, due to their engineering background, engineers may forget that educating a person is not the same as designing a machine, since a machine has a well-defined goal, whilst a person is capable to self-propose his or her own objectives. The ethical implications are clear: educating a free person must leave space for creativity and self-determination in his or her own discovery of the way towards personal and professional fulfillment, which cannot consist only in achieving goals selected by others. We present here an argument that is applicable to most fields of engineering. However, the dis-analogy between educating students and programming robots may have a particular appeal to software engineers and computer scientists. We think the consideration of three different stages in the educational process may be useful to engineers when they act as educators. We claim that the three stages (instructing, training and mentoring) are essential to engineering education. In particular, education is incomplete if the third stage is not reached. Moreover, mentoring (the third stage aimed at developing creativity and self-determination) is incompatible with an educational assessment framework that considers the goals of the engineer are always given by others. In our view, then, an integral education is not only beyond programming the behavior of students, but also beyond having them reach those given goals.

Artificial intelligence empowering public health education: prospects and challenges.

Artificial Intelligence (AI) is revolutionizing public health education through its capacity for intricate analysis of large-scale health datasets and the tailored dissemination of health-related information and interventions. This article conducts a profound exploration into the integration of AI within public health, accentuating its scientific foundations, prospective progress, and practical application scenarios. It underscores the transformative potential of AI in crafting individualized educational programs, developing sophisticated behavioral models, and informing the creation of health policies. The manuscript strives to thoroughly evaluate the extant landscape of AI applications in public health, scrutinizing critical challenges such as the propensity for data bias and the imperative of safeguarding privacy. By dissecting these issues, the article contributes to the conversation on how AI can be harnessed responsibly and effectively, ensuring that its application in public health education is both ethically grounded and equitable. The paper's significance is multifold: it aims to provide a blueprint for policy formulation, offer actionable insights for public health authorities, and catalyze the progression of health interventions toward increasingly sophisticated and precise approaches. Ultimately, this research anticipates fostering an environment where AI not only augments public health education but also does so with a steadfast commitment to the principles of justice and inclusivity, thereby elevating the standard and reach of health education initiatives globally.

Perspectives on Artificial Intelligence in Nursing in Asia.

Artificial intelligence (AI) is reshaping health care, including nursing, across Asia, presenting opportunities to improve patient care and outcomes. This viewpoint presents our perspective and interpretation of the current AI landscape, acknowledging its evolution driven by enhanced processing capabilities, extensive data sets, and refined algorithms. Notable applications in countries such as Singapore, South Korea, Japan, and China showcase the integration of AI-powered technologies such as chatbots, virtual assistants, data mining, and automated risk assessment systems. This paper further explores the transformative impact of AI on nursing education, emphasizing personalized learning, adaptive approaches, and AI-enriched simulation tools, and discusses the opportunities and challenges of these developments. We argue for the harmonious coexistence of traditional nursing values with AI innovations, marking a significant stride toward a promising health care future in Asia.

Artificial CT images can enhance variation of case images in diagnostic radiology skills training.

OBJECTIVES: We sought to investigate if artificial medical images can blend with original ones and whether they adhere to the variable anatomical constraints provided. METHODS: Artificial images were generated with a generative model trained on publicly available standard and low-dose chest CT images (805 scans; 39,803 2D images), of which 17% contained evidence of pathological formations (lung nodules). The test set (90 scans; 5121 2D images) was used to assess if artificial images (512 × 512 primary and control image sets) blended in with original images, using both quantitative metrics and expert opinion. We further assessed if pathology characteristics in the artificial images can be manipulated. RESULTS: Primary and control artificial images attained an average objective similarity of 0.78 ± 0.04 (ranging from 0 [entirely dissimilar] to 1[identical]) and 0.76 ± 0.06, respectively. Five radiologists with experience in chest and thoracic imaging provided a subjective measure of image quality; they rated artificial images as 3.13 ± 0.46 (range of 1 [unrealistic] to 4 [almost indistinguishable to the original image]), close to their rating of the original images (3.73 ± 0.31). Radiologists clearly distinguished images in the control sets (2.32 ± 0.48 and 1.07 ± 0.19). In almost a quarter of the scenarios, they were not able to distinguish primary artificial images from the original ones. CONCLUSION: Artificial images can be generated in a way such that they blend in with original images and adhere to anatomical constraints, which can be manipulated to augment the variability of cases. CRITICAL RELEVANCE STATEMENT: Artificial medical images can be used to enhance the availability and variety of medical training images by creating new but comparable images that can blend in with original images. KEY POINTS: • Artificial images, similar to original ones, can be created using generative networks. • Pathological features of artificial images can be adjusted through guiding the network. • Artificial images proved viable to augment the depth and broadening of diagnostic training.

Using ChatGPT as a Learning Tool in Acupuncture Education: Comparative Study.

BACKGROUND: ChatGPT (Open AI) is a state-of-the-art artificial intelligence model with potential applications in the medical fields of clinical practice, research, and education. OBJECTIVE: This study aimed to evaluate the potential of ChatGPT as an educational tool in college acupuncture programs, focusing on its ability to support students in learning acupuncture point selection, treatment planning, and decision-making. METHODS: We collected case studies published in Acupuncture in Medicine between June 2022 and May 2023. Both ChatGPT-3.5 and ChatGPT-4 were used to generate suggestions for acupuncture points based on case presentations. A Wilcoxon signed-rank test was conducted to compare the number of acupuncture points generated by ChatGPT-3.5 and ChatGPT-4, and the overlapping ratio of acupuncture points was calculated. RESULTS: Among the 21 case studies, 14 studies were included for analysis. ChatGPT-4 generated significantly more acupuncture points (9.0, SD 1.1) compared to ChatGPT-3.5 (5.6, SD 0.6; P<.001). The overlapping ratios of acupuncture points for ChatGPT-3.5 (0.40, SD 0.28) and ChatGPT-4 (0.34, SD 0.27; P=.67) were not significantly different. CONCLUSIONS: ChatGPT may be a useful educational tool for acupuncture students, providing valuable insights into personalized treatment plans. However, it cannot fully replace traditional diagnostic methods, and further studies are needed to ensure its safe and effective implementation in acupuncture education.

A Knowledge Query Network Model Based on Rasch Model Embedding for Personalized Online Learning.

The vigorous development of online education has produced massive amounts of education data. How to mine and analyze education big data has become an urgent problem in the field of education and big data knowledge engineering. As for the dynamic learning data, knowledge tracing aims to track learners' knowledge status over time by analyzing the learners' exercise data, so as to predict their performance in the next time step. Deep learning knowledge tracking performs well, but they mainly model the knowledge components while ignoring the personalized information of questions and learners, and provide limited interpretability in the interaction between learners' knowledge status and questions. A context-aware attentive knowledge query network (CAKQN) model is proposed in this paper, which combines flexible neural network models with interpretable model components inspired by psychometric theory. We use the Rasch model to regularize the embedding of questions and learners' interaction tuples, and obtain personalized representations from them. In addition, the long-term short-term memory network and monotonic attention mechanism are used to mine the contextual information of learner interaction sequences and question sequences. It can not only retain the ability to model sequences, but also use the monotonic attention mechanism with exponential decay term to extract the hidden forgetting behavior and other characteristics of learners in the learning process. Finally, the vector dot product is used to simulate the interaction between the learners' knowledge state and questions to improve the interpretability. A series of experimental results on 4 real-world online learning datasets show that CAKQN has the best performance, and its AUC value is improved by an average of 2.945% compared with the existing optimal model. Furthermore, the CAKQN proposed in this paper can not only track learners' knowledge status like other models but also model learners' forgetting behavior. In the future, our research will have high application value in the realization of personalized learning strategies, teaching interventions, and resource recommendations for intelligent online education platforms.

Demographic, Clinical, and Psychosocial Predictors of Change in Medication Adherence in the Support, Educate, Empower Program.

PURPOSE: To investigate whether demographic, clinical, or psychosocial factors act as moderators of change in medication adherence in the Support, Educate, Empower (SEE) program. DESIGN: Prospective, single-arm pilot study with a pre-post design. PARTICIPANTS: Patients with glaucoma aged ≥ 40 years and taking ≥ 1 glaucoma medication were recruited from the University of Michigan Kellogg Eye Center. Those who had electronically measured adherence ≤ 80% in the 3-month eligibility monitoring period were enrolled in the SEE program. METHODS: Medication adherence was monitored electronically during the 7-month intervention and calculated as the percentage of doses taken correctly. Change in adherence at different points in the SEE program and cumulative change in adherence were modeled with linear regression, and baseline demographic, clinical, and psychosocial factors were investigated for significant associations. MAIN OUTCOME MEASURES: Demographic, clinical, and psychosocial variables associated with change in medication adherence in the SEE program. RESULTS: Thirty-nine participants completed the SEE program. These participants were on average 63.9 years old (standard deviation [SD], 10.7 years), 56% (n = 22) were male, 44% (n = 17) were White, and 49% (n = 19) were Black. Medication adherence improved from an average of 59.9% (SD, 18.5%) at baseline to 83.6% (SD, 17.5%) after the final SEE session, for an increase of 23.7% (SD, 17.5%). Although participants with lower income (< $25 000 and $25 000-50 000 vs. >$50 000) had lower baseline adherence (48.4% and 64.1% vs. 70.4%), these individuals had greater increases in adherence during the first month of medication reminders (19.6% and 21.6% vs. 10.2%; P = 0.05 and P = 0.007, respectively). Participants taking fewer glaucoma medications also had significantly greater increases in adherence with medication reminders (P < 0.001). Those with higher levels of glaucoma-related distress (GD) had lower baseline adherence and greater increases in adherence with glaucoma coaching (P = 0.06). CONCLUSIONS: Patient-level factors associated with relatively greater improvements in medication adherence through the SEE Program included lower income, fewer glaucoma medications, and increased GD. These findings demonstrate that the SEE program can improve glaucoma self-management even among participants with social and psychological barriers to medication adherence.

The Effect of a Personalized Oral Health Education Program on Periodontal Health in an At-Risk Population: A Randomized Controlled Trial.

While periodontal disease is associated with many risk factors, socioeconomically disadvantaged communities experience the highest disease burden. The aim of this study was to evaluate the effectiveness of a personalized oral health education program, in combination with routine dental treatment, in participants from a low socioeconomic community. We used a randomized, controlled, examiner-blinded clinical trial. A total of 579 participants (aged 18-60 years) were randomly grouped: the intervention group (n = 292) received a personalized oral health education program in combination with routine dental care and the control group (n = 287) received routine dental care. All participants were assessed for improvement in oral health care behaviors, dental plaque, and periodontal status at baseline, 12 months, and 24 months. We found a significant drop (p < 0.001) in the plaque indices, Periodontal Probing Depths (PPD) and Bleeding on Probing (BOP) between baseline and the 12-month follow-up for both groups. For BOP, the number of sites positive was significantly different between baseline and the 24-month follow-up (p = 0.037). No differences were found between the two groups for any evaluated clinical outcome. The personalized oral health education program used in the current study did not appear to add significant improvement to clinical outcomes of periodontal health compared with routine restorative dental care per se.

Corrigendum: Educational Data Mining Techniques for Student Performance Prediction: Method Review and Comparison Analysis.

[This corrects the article DOI: 10.3389/fpsyg.2021.698490.].

Educational Data Mining Techniques for Student Performance Prediction: Method Review and Comparison Analysis.

Student performance prediction (SPP) aims to evaluate the grade that a student will reach before enrolling in a course or taking an exam. This prediction problem is a kernel task toward personalized education and has attracted increasing attention in the field of artificial intelligence and educational data mining (EDM). This paper provides a systematic review of the SPP study from the perspective of machine learning and data mining. This review partitions SPP into five stages, i.e., data collection, problem formalization, model, prediction, and application. To have an intuition on these involved methods, we conducted experiments on a data set from our institute and a public data set. Our educational dataset composed of 1,325 students, and 832 courses was collected from the information system, which represents a typical higher education in China. With the experimental results, discussions on current shortcomings and interesting future works are finally summarized from data collections to practices. This work provides developments and challenges in the study task of SPP and facilitates the progress of personalized education.

Effects of a Personalized Nurse-Led Educational Program for New Patients Receiving Oral Anticoagulant Therapy after Mechanical Heart Valve Prosthesis Implantation on Adherence to Treatment.

BACKGROUND: Life-long anticoagulant therapy is mandatory for patients who undergo heart valve replacement with implantation of a mechanical prosthesis. The aim of this study was to investigate the effects of a nurse-led patient educational program concerning oral anticoagulant therapy intake after heart valve replacement surgery on patients' knowledge of important parameters of anticoagulant administration. METHODS: In this single-center study, 200 patients who underwent surgical implantation of a mechanical prosthesis were divided into 2 groups. The control group received the basic education concerning oral anticoagulants, while the intervention group received a personalized educational program. RESULTS: Personalized education was correlated with a better regulation of therapeutic international normalized ratio (INR) levels and adequate knowledge among patients. Therapeutic levels of INR were achieved in 45% of the patients during the first month, 71% in the third month, and 89% in the sixth month after discharge in the intervention group, compared to 25%, 47%, and 76% in the control group, respectively. Patients' satisfaction with the information was higher in the intervention group than in the control group. The percentage of satisfaction reached 80% for the intervention group versus 37% for the patients of the control group. CONCLUSION: The implementation of the nurse-led educational programs was associated with improved clinical results and increased adherence to oral anticoagulant treatment.

Personalizing HRI in Musical Instrument Practicing: The Influence of Robot Roles (Evaluative Versus Nonevaluative) on the Child's Motivation for Children in Different Learning Stages.

Learning to play a musical instrument involves skill learning and requires long-term practicing to reach expert levels. Research has already proven that the assistance of a robot can improve children's motivation and performance during practice. In an earlier study, we showed that the specific role (evaluative role versus nonevaluative role) the robot plays can determine children's motivation and performance. In the current study, we argue that the role of the robot has to be different for children in different learning stages (musical instrument expertise levels). Therefore, this study investigated whether children in different learning stages would have higher motivation when assisted by a robot in different supporting roles (i.e., evaluative role versus nonevaluative role). We conducted an empirical study in a real practice room of a music school with 31 children who were at different learning stages (i.e., beginners, developing players, and advanced players). In this study, every child practiced for three sessions: practicing alone, assisted by the evaluative robot, or assisted by the nonevaluative robot (in a random order). We measured motivation by using a questionnaire and analyzing video data. Results showed a significant interaction between condition (i.e., alone, evaluative robot, and nonevaluative robot) and learning stage groups indicating that children in different learning stage groups had different levels of motivation when practicing alone or with an evaluative or nonevaluative robot. More specifically, beginners had higher persistence when practicing with the nonevaluative robot, while advanced players expressed higher motivation after practicing with a robot than alone, but no difference was found between the two robot roles. Exploratory results also indicated that gender might have an interaction effect with the robot roles on child's motivation in music practice with social robots. This study offers more insight into the child-robot interaction and robot role design in musical instrument learning. Specifically, our findings shed light on personalization in HRI, that is, from adapting the role of the robot to the characteristics and the development level of the user.

Artificial intelligence-based education assists medical students' interpretation of hip fracture.

BACKGROUND: With recent transformations in medical education, the integration of technology to improve medical students' abilities has become feasible. Artificial intelligence (AI) has impacted several aspects of healthcare. However, few studies have focused on medical education. We performed an AI-assisted education study and confirmed that AI can accelerate trainees' medical image learning. MATERIALS: We developed an AI-based medical image learning system to highlight hip fracture on a plain pelvic film. Thirty medical students were divided into a conventional (CL) group and an AI-assisted learning (AIL) group. In the CL group, the participants received a prelearning test and a postlearning test. In the AIL group, the participants received another test with AI-assisted education before the postlearning test. Then, we analyzed changes in diagnostic accuracy. RESULTS: The prelearning performance was comparable in both groups. In the CL group, postlearning accuracy (78.66 ± 14.53) was higher than prelearning accuracy (75.86 ± 11.36) with no significant difference (p = .264). The AIL group showed remarkable improvement. The WithAI score (88.87 ± 5.51) was significantly higher than the prelearning score (75.73 ± 10.58, p < 0.01). Moreover, the postlearning score (84.93 ± 14.53) was better than the prelearning score (p < 0.01). The increase in accuracy was significantly higher in the AIL group than in the CL group. CONCLUSION: The study demonstrated the viability of AI for augmenting medical education. Integrating AI into medical education requires dynamic collaboration from research, clinical, and educational perspectives.

Advancing Our Pharmacy Reformation - Accelerating Education and Practice Transformation: Report of the 2019-2020 Argus Commission.

The Argus Commission examined changes that should be considered by colleges and schools of pharmacy to meet the bold aim of better integrating pharmacists' and physicians' practices articulated by President Sorensen. The Commission assessed the readiness of pharmacy educators to contribute to the acceleration of practice transformation. The primary focus of the report is on how the doctor of pharmacy curriculum and post-graduate training might be modified and better aligned to ensure that graduates complete their education ready to engage in roles partnered with primary care clinicians. The aim is to achieve comprehensive medication management and other pharmacist patient care services as standards of care. The Argus Commission provides preliminary recommendations for new or more intensified priorities by the 2020-21 AACP Strategic Planning Committee as they update the AACP plan. This includes the recommendation that AACP should create the Center for Academic Innovation and Practice Transformation, a hub to coordinate many current and emerging activities relevant to accelerating change in pharmacy education and practice.

Personalized Education to Increase Interest.

A long-standing ideal of school education has been to connect instruction to a student's life outside school in order to render subject matter interesting. New technologies enable instructors to personalize learning materials to increase situational interest. After distinguishing three main methods to personalize education (context personalization, choice, and active personalization), we review recent intervention studies designed to increase situational interest, which is necessary for the emergence of individual interest. Across all three kinds of interventions, some studies point to the possibility of increasing interest for students low in initial interest. Despite progress in developing personalized interventions for school practice, research on the theoretical mechanisms behind the success of the interventions has just begun.